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--- |
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language: |
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- en |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:156483 |
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- loss:AdaptiveLayerLoss |
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- loss:CoSENTLoss |
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- loss:GISTEmbedLoss |
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- loss:OnlineContrastiveLoss |
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- loss:MultipleNegativesSymmetricRankingLoss |
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base_model: microsoft/deberta-v3-small |
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datasets: |
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- sentence-transformers/all-nli |
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- sentence-transformers/stsb |
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- tals/vitaminc |
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- nyu-mll/glue |
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- allenai/scitail |
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- sentence-transformers/xsum |
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- sentence-transformers/sentence-compression |
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- allenai/sciq |
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- allenai/qasc |
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- allenai/openbookqa |
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- sentence-transformers/msmarco-msmarco-distilbert-base-v3 |
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- sentence-transformers/natural-questions |
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- sentence-transformers/trivia-qa |
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- sentence-transformers/quora-duplicates |
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- sentence-transformers/gooaq |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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widget: |
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- source_sentence: Centrosome-independent mitotic spindle formation in vertebrates. |
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sentences: |
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- Birds pair up with the same bird in mating season. |
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- We use voltage to keep track of electric potential energy. |
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- A mitotic spindle forms from the centrosomes. |
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- source_sentence: Three women in festive dresses are dancing in a large city street |
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while a large crowd is behind them holding up a banner. |
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sentences: |
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- Three women dance in front of a large crowd |
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- A person is taking a little girl with a toy from an Airman. |
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- A man is sitting. |
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- source_sentence: A young child is washing his hands with soap in the kitchen. |
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sentences: |
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- A surfer catches a wave. |
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- Male in brown leather jacket and black slacks, looking down at his phone |
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- The child uses soap to wash his hands. |
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- source_sentence: A group of children enjoy their time on the beach. |
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sentences: |
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- People watch another person do a trick. |
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- Children are at the beach. |
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- A person is kicking. |
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- source_sentence: Are juiveniles capable of reproduction? |
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sentences: |
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- In at least some species, juveniles are capable of reproduction before reaching |
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the adult size and shape. |
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- The Alpine Rhine begins in the most western part of the Swiss canton of Graubünden, |
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and later forms the border between Switzerland to the West and Liechtenstein and |
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later Austria to the East. |
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- Private school numbers have been in decline since the mid-1970s as a result of |
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many private schools opting to become state-integrated schools, mostly due of |
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financial difficulties stemming from changes in student numbers and/or the economy. |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: SentenceTransformer based on microsoft/deberta-v3-small |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test |
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type: sts-test |
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metrics: |
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- type: pearson_cosine |
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value: 0.7784505751254295 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.7817593717132717 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.7551849979009766 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.7430091894692992 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.7474026307882491 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.7344164621317546 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.5592770538627994 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.550085155372067 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.7784505751254295 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.7817593717132717 |
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name: Spearman Max |
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--- |
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|
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# SentenceTransformer based on microsoft/deberta-v3-small |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli), [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb), [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum), [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa), [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) and [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Datasets:** |
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- [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) |
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- [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb) |
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- [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) |
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- [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) |
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- [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) |
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- [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) |
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- [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) |
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- [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) |
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- [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) |
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- [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) |
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- [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa) |
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- [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) |
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- [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) |
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- [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) |
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- [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) |
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- [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) |
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- **Language:** en |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("bobox/DeBERTaV3-TR-AllSoft-HT") |
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# Run inference |
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sentences = [ |
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'Are juiveniles capable of reproduction?', |
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'In at least some species, juveniles are capable of reproduction before reaching the adult size and shape.', |
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'Private school numbers have been in decline since the mid-1970s as a result of many private schools opting to become state-integrated schools, mostly due of financial difficulties stemming from changes in student numbers and/or the economy.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Dataset: `sts-test` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.7785 | |
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| **spearman_cosine** | **0.7818** | |
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| pearson_manhattan | 0.7552 | |
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| spearman_manhattan | 0.743 | |
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| pearson_euclidean | 0.7474 | |
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| spearman_euclidean | 0.7344 | |
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| pearson_dot | 0.5593 | |
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| spearman_dot | 0.5501 | |
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| pearson_max | 0.7785 | |
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| spearman_max | 0.7818 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Datasets |
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#### nli-pairs |
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* Dataset: [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
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* Size: 12,500 training samples |
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* Columns: <code>sentence1</code> and <code>sentence2</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 16.62 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 29 tokens</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | |
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|:---------------------------------------------------------------------------|:-------------------------------------------------| |
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| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | |
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| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | |
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| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | |
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* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
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```json |
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{ |
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"loss": "GISTEmbedLoss", |
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"n_layers_per_step": -1, |
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"last_layer_weight": 1, |
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"prior_layers_weight": 0.5, |
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"kl_div_weight": 1.25, |
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"kl_temperature": 0.75 |
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} |
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``` |
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#### sts-label |
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* Dataset: [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) |
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* Size: 5,749 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 9.81 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.74 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------| |
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| <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> | |
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| <code>A man is playing a large flute.</code> | <code>A man is playing a flute.</code> | <code>0.76</code> | |
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| <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_cos_sim" |
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} |
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``` |
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#### vitaminc-pairs |
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* Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) |
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* Size: 15,594 training samples |
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* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | label | sentence1 | sentence2 | |
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|:--------|:-----------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | int | string | string | |
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| details | <ul><li>1: 100.00%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.05 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 38.52 tokens</li><li>max: 187 tokens</li></ul> | |
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* Samples: |
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| label | sentence1 | sentence2 | |
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|:---------------|:--------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>1</code> | <code>Mary Kom had a budget of more than 160 million .</code> | <code>Made on a budget of 180 million including production , prints and advertising , Mary Kom earned 200 million , and before release recouped more than its cost from brand tie-ups .</code> | |
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| <code>1</code> | <code>Taylor Swift is a singer-songwriter .</code> | <code>American singer-songwriter Taylor Swift recorded with Nashville-based Big Machine Records .</code> | |
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| <code>1</code> | <code>Blake voluntarily joined the Army .</code> | <code>In 1950 , Blake joined the Army .</code> | |
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* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
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```json |
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{ |
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"loss": "GISTEmbedLoss", |
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"n_layers_per_step": -1, |
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"last_layer_weight": 1, |
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"prior_layers_weight": 0.5, |
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"kl_div_weight": 1.25, |
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"kl_temperature": 0.75 |
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} |
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``` |
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#### qnli-contrastive |
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* Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) |
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* Size: 8,000 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | label | |
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|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 13.75 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 35.06 tokens</li><li>max: 146 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
|
|:-------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>What city almost exclusively celebrates the Carnival?</code> | <code>Carnival has been celebrated for centuries.</code> | <code>0</code> | |
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| <code>Who had a special tribute at the finale?</code> | <code>Many figures from the show's past, including Paula Abdul, made an appearance.</code> | <code>0</code> | |
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| <code>In 1849 where did Chopin live?</code> | <code>During the summer of 1849, his friends found him an apartment in Chaillot, out of the centre of the city, for which the rent was secretly subsidised by an admirer, Princess Obreskoff.</code> | <code>0</code> | |
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* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
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```json |
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{ |
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"loss": "OnlineContrastiveLoss", |
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"n_layers_per_step": -1, |
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"last_layer_weight": 1, |
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"prior_layers_weight": 0.5, |
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"kl_div_weight": 1.25, |
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"kl_temperature": 0.75 |
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} |
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``` |
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#### scitail-pairs-qa |
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* Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) |
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* Size: 14,987 training samples |
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* Columns: <code>sentence2</code> and <code>sentence1</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence2 | sentence1 | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 15.67 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.94 tokens</li><li>max: 33 tokens</li></ul> | |
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* Samples: |
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| sentence2 | sentence1 | |
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|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| |
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| <code>Circle graphs are useful for showing percents of a whole.</code> | <code>What type of graph is useful for showing percents of a whole?</code> | |
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| <code>The first living cells may have evolved around 4 billion years ago.</code> | <code>The first living cells may have evolved around how long ago?</code> | |
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| <code>Chinook winds occur when air is forced over a mountain range.</code> | <code>What type of winds occur when air is forced over a mountain range?</code> | |
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* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
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```json |
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{ |
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"loss": "GISTEmbedLoss", |
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"n_layers_per_step": -1, |
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"last_layer_weight": 1, |
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"prior_layers_weight": 0.5, |
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"kl_div_weight": 1.25, |
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"kl_temperature": 0.75 |
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} |
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``` |
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#### scitail-pairs-pos |
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* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) |
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* Size: 8,600 training samples |
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* Columns: <code>sentence1</code> and <code>sentence2</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
|
| details | <ul><li>min: 7 tokens</li><li>mean: 24.07 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.69 tokens</li><li>max: 39 tokens</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------| |
|
| <code>For pure water, the boiling point is 100 degrees Celsius (212 Fahrenheit) at one atmosphere of pressure, and the melting point is 0 degrees Celsius (32 degrees Fahrenheit) at one atmosphere of pressure.</code> | <code>In celsius, the boiling point of water is one hundred degrees.</code> | |
|
| <code>Deductive logic starts with a general principle, premise, or definition and then analyzes its more specific implications.</code> | <code>Making a specific statement based on a general principle is the definition of deductive reasoning.</code> | |
|
| <code>Commonly only components of the blood, such as red blood cells, white blood cells, plasma, clotting factors, and platelets are used.</code> | <code>Blood is made up of red blood cells, white blood cells, platelets, and plasma.</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "GISTEmbedLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
#### xsum-pairs |
|
|
|
* Dataset: [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206) |
|
* Size: 2,500 training samples |
|
* Columns: <code>sentence1</code> and <code>sentence2</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | |
|
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 2 tokens</li><li>mean: 340.67 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.73 tokens</li><li>max: 70 tokens</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Media playback is not supported on this device<br>The duo opened up a sizeable gap in the cycle before Olympic champion Alistair ran clear with 2.5km left to win in one hour, 46 minutes and 51 seconds.<br>Jonny crossed the line 12secs later with Spain's Fernando Alarza third.<br>Bermuda's Flora Duffy produced a superb display to win the women's race.<br>The 29-year-old, who triumphed in the World Series event in Yokohama last month, dominated the race, winning in a time of one hour, 57 minutes and two seconds.<br>America's Taylor Spivey was second, 1min 30secs behind Duffy, with Italy's Alice Betto third.<br>Media playback is not supported on this device<br>For the first time in a World Series triathlon event, four of the top five finishers were from the same country, thanks to Britain's Adam Bowden and Tom Bishop, who finished fourth and fifth respectively.<br>Once again, though, the Brownlees dominated in their first race together since Alistair carried stricken Jonny over the line in last year's World Series finale in Cozumel in Mexico.<br>They were among the first out of the water, in a field that did not contain world champion Mario Mola or Javier Gomez, and were part of the lead group on the bike leg alongside Pierre Le Corre and Aurelien Raphael before breaking away from the French pair after the first 10km.<br>The gap was more than a minute when they began the run, during which Alistair again showed his superiority.<br>"It was really special," Alistair told BBC Sport. "You can never expect to win a race and know what is going to happen.<br>"I was involved in it from the very start, bidding to get it here and doing some course organisation. The last few hundred metres leading up to Millennium Square was amazing. I will remember this experience for the rest of my career.<br>"We were riding really hard on the bikes. For the first three laps of the circuit, we were probably riding as hard as we ever have. That took it out of us on the run."<br>Jonny said: "It was a hard way to do that race, to be the two of us from the end of the swim.<br>"If I want to beat Alistair, that is not the way to do it. I basically turned it into a long-distance hard man's race and he is a bit harder than me.<br>"We wanted people to work with us but they weren't strong enough. I was feeling good at the start of the run but injuries and illnesses mean I have missed quite a lot of training this year."<br>Alarza heads the World Series rankings with 2,743 points, 679 ahead of compatriot Mola.<br>Media playback is not supported on this device<br>Three-time British Triathlon champion Annie Emmerson, speaking to BBC Sport<br>It is one of the best races I have ever seen. What those guys do - coming into town and staying away from that bike pack, which was literally 10 lengths behind - is amazing.<br>It was the Brownlee show. For all these guys that have come out here today, they have made it special.<br>Once again, though, Alistair has shown that he is just the best that there is.<br>Media playback is not supported on this device<br>Jessica Learmonth was the highest-placed Briton in the women's race, with a World Series personal best of sixth after a final surge took her past Australia's Gillian Backhouse.<br>The Leeds-born athlete was leading during the cycle, but dropped back to help team-mate Non Stanford.<br>"We spoke before the race and that was the tactic [to work as a team]," Learmonth told BBC Sport. "I was more than happy to drop back and help Non push on. It is something we need to practice and this is a great place to do it."<br>Welsh former world champion Stanford finished 11th.<br>"It wasn't my best time," she said. "I have had a rough couple of weeks since Yokohama. I gave it as much as I could for as long as possible, but when I got to the run I had nothing left.<br>"We decided we were going to work as a team and that Jess would wait, so I did everything I could to get back up to her after the swim and to get us in a good position but I don't think I contributed very much, unfortunately.<br>"It is just a matter of getting myself round and the crowds helped me do that. I know it is a cliche but the support was fantastic."<br>The women's World Series rankings are led by American Kirsten Kasper, who finished fourth on Sunday and now has 2,049 points, 175 more than compatriot Katie Zaferes.<br>Team GB triathlete Vicky Holland, speaking to BBC Sport<br>These guys [Jessica and Non] are my team-mates and friends so I know when they have had troubles and injuries and illnesses and setbacks. I don't think there is a single person in the team today who has had a smooth run-in.<br>These two gave it everything. There is no doubt what they put into the race. Jess did a great job and went back for Non.<br>The front group were working together, motivated well by Flora Duffy, and the gap kept growing as they were motivated by the podiums.</code> | <code>Alistair Brownlee beat brother Jonny as they repeated last year's achievement of a British one-two at the World Series Triathlon event in their home city of Leeds.</code> | |
|
| <code>Resuming in a strong position at 182-3, the tourists were bundled out for 242 in a dreadful morning session in Dubai.<br>Joe Root, Ben Stokes and the out-of-form Jos Buttler fell victim to Wahab Riaz's pace, before Yasir Shah's leg-spin bamboozled the lower order.<br>By the close, Pakistan reached 222-3, a lead of 358 runs, with Misbah ul-Haq 87 not out and Younus Khan 71 not out.<br>After coming so close to winning the first Test, and contesting the first two days of this match fiercely, this was a hugely disappointing day for the tourists - and one that seriously imperils their chances of getting a result in this series.<br>Alastair Cook's men will have to hope they can dismiss the hosts quickly on day four, then set about a huge run chase - but their chances of avoiding defeat on a turning fourth-innings track with the wily Yasir in operation appear slim.<br>England's chances of getting close to, or even overhauling, Pakistan's first-innings total of 378 were always likely to depend on the influential Root - unbeaten on 76 overnight.<br>But the day got off to the worst possible when the Yorkshire batsman swished at a wide delivery from Wahab and was caught behind for 88.<br>From then on, England's increasing over-reliance on Root and Cook was horribly exposed as the remaining six wickets tumbled for only 30 runs.<br>Wahab, bowling with real hostility to exploit England's uncertainty against genuine pace, had Stokes (4) and Buttler (0) caught behind in quick succession in a superb spell of 3-15 in nine overs.<br>Then it was over to Yasir - bowling in tandem with Wahab for the first 18 overs of the day - who justified his status as the world's top-ranked spin bowler with a display of high-class leg-spin.<br>Memories of England's awful performances against spin on their last tour of the UAE were revived as Adil Rashid (0), Jonny Bairstow (46) and Mark Wood (1) all perished with poor shots to Yasir.<br>After conceding a first-innings lead of 136, England desperately needed early wickets, and their seamers got two quick breakthroughs.<br>James Anderson dismissed Shan Masood (1) for the fourth time in the series before Shoaib Malik (7) was bowled trying to drive Mark Wood.<br>But England could not capitalise on that good start as their spinners struggled.<br>Mohammad Hafeez smashed Moeen Ali and Root for sixes before he was caught at slip off Wood for 51.<br>That brought first-innings centurion Misbah to the crease with Younus - a partnership with a combined age of 78 and one that has thwarted England throughout this series.<br>Playing cautiously against the seamers and looking to dominate the spinners, the duo - who have now put on a fifty partnership in nine out of the last 10 occasions they have been at the crease together - batted England further and further out of contention.<br>To win, the tourists will have to surpass the highest-ever fourth-innings chase at Dubai - 137 - and record at least the fifth-highest run-chase this century.<br>While Stokes is averaging just 24 since the start of the Ashes and Bairstow remains without a Test century, it is the form of Buttler that is the biggest concern in England's malfunctioning middle order.<br>The wicketkeeper - whose average after his first eight Tests was 52.67 - is averaging just 13.55 in his last seven matches against Australia and Pakistan, with 149 runs in 11 innings.<br>"Buttler's numbers are not good," said former England captain Michael Vaughan on Test Match Special. "He's got a huge problem: he's not lining up the ball, he's playing it two feet away from his body."<br>In addition, even his usually reliable keeping faltered as he dropped a simple chance presented by Shan Masood off the bowling of Stuart Broad, and saw another chance fly between him and Joe Root at first slip.<br>Bairstow is also capable of taking the gloves and James Taylor could replace Buttler in the middle order should England decide to change their side for the third Test in Sharjah.<br>Ex-England spinner Graeme Swann on BBC Test Match Special: "I think there will be two changes [for the next Test]. The way they're going, they're going to lose unless something ridiculous happens.<br>"Barring a big score from Buttler or Bell, I think they'll be asked to sit out the last Test. That will allow you to bring in an opening batsman in Alex Hales, and James Taylor at three, with Moeen Ali moving to seven. He's not an opening bat, he showed in Abu Dhabi it's not his natural game."<br>Michael Vaughan on BBC Test Match Special: "My frustration with England is that they keep having these horrible sessions that lose them the game. Losing seven wickets for 36 runs... it's over. You can say I'm being negative but this game is done."</code> | <code>England require their best-ever fourth-innings run chase to beat Pakistan in the second Test after a dire third day.</code> | |
|
| <code>The two-vehicle accident happened close the entrance to Brucklay Castle, near Maud.<br>The road has been closed in both directions.<br>So far there are no indications of any injuries or how many people are involved.</code> | <code>An air ambulance and emergency crews are at the scene of a serious crash on a stretch of the A891 in Aberdeenshire.</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "GISTEmbedLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
#### compression-pairs |
|
|
|
* Dataset: [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) |
|
* Size: 8,000 training samples |
|
* Columns: <code>sentence1</code> and <code>sentence2</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | |
|
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 10 tokens</li><li>mean: 31.89 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.21 tokens</li><li>max: 28 tokens</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | |
|
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------| |
|
| <code>The USHL completed an expansion draft on Monday as 10 players who were on the rosters of USHL teams during the 2009-10 season were selected by the League's two newest entries, the Muskegon Lumberjacks and Dubuque Fighting Saints.</code> | <code>USHL completes expansion draft</code> | |
|
| <code>Major League Baseball Commissioner Bud Selig will be speaking at St. Norbert College next month.</code> | <code>Bud Selig to speak at St. Norbert College</code> | |
|
| <code>It's fresh cherry time in Michigan and the best time to enjoy this delicious and nutritious fruit.</code> | <code>It's cherry time</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesSymmetricRankingLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 1.5 |
|
} |
|
``` |
|
|
|
#### sciq_pairs |
|
|
|
* Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) |
|
* Size: 11,679 training samples |
|
* Columns: <code>sentence1</code> and <code>sentence2</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | |
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 7 tokens</li><li>mean: 17.26 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 84.37 tokens</li><li>max: 512 tokens</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>What type of organism is commonly used in preparation of foods such as cheese and yogurt?</code> | <code>Mesophiles grow best in moderate temperature, typically between 25°C and 40°C (77°F and 104°F). Mesophiles are often found living in or on the bodies of humans or other animals. The optimal growth temperature of many pathogenic mesophiles is 37°C (98°F), the normal human body temperature. Mesophilic organisms have important uses in food preparation, including cheese, yogurt, beer and wine.</code> | |
|
| <code>What phenomenon makes global winds blow northeast to southwest or the reverse in the northern hemisphere and northwest to southeast or the reverse in the southern hemisphere?</code> | <code>Without Coriolis Effect the global winds would blow north to south or south to north. But Coriolis makes them blow northeast to southwest or the reverse in the Northern Hemisphere. The winds blow northwest to southeast or the reverse in the southern hemisphere.</code> | |
|
| <code>Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always what?</code> | <code>Summary Changes of state are examples of phase changes, or phase transitions. All phase changes are accompanied by changes in the energy of a system. Changes from a more-ordered state to a less-ordered state (such as a liquid to a gas) areendothermic. Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always exothermic. The conversion of a solid to a liquid is called fusion (or melting). The energy required to melt 1 mol of a substance is its enthalpy of fusion (ΔHfus). The energy change required to vaporize 1 mol of a substance is the enthalpy of vaporization (ΔHvap). The direct conversion of a solid to a gas is sublimation. The amount of energy needed to sublime 1 mol of a substance is its enthalpy of sublimation (ΔHsub) and is the sum of the enthalpies of fusion and vaporization. Plots of the temperature of a substance versus heat added or versus heating time at a constant rate of heating are calledheating curves. Heating curves relate temperature changes to phase transitions. A superheated liquid, a liquid at a temperature and pressure at which it should be a gas, is not stable. A cooling curve is not exactly the reverse of the heating curve because many liquids do not freeze at the expected temperature. Instead, they form a supercooled liquid, a metastable liquid phase that exists below the normal melting point. Supercooled liquids usually crystallize on standing, or adding a seed crystal of the same or another substance can induce crystallization.</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "GISTEmbedLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
#### qasc_pairs |
|
|
|
* Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) |
|
* Size: 8,134 training samples |
|
* Columns: <code>id</code>, <code>sentence1</code>, and <code>sentence2</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | id | sentence1 | sentence2 | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | string | |
|
| details | <ul><li>min: 17 tokens</li><li>mean: 21.35 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.47 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 35.55 tokens</li><li>max: 66 tokens</li></ul> | |
|
* Samples: |
|
| id | sentence1 | sentence2 | |
|
|:--------------------------------------------|:---------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>3E7TUJ2EGCLQNOV1WEAJ2NN9ROPD9K</code> | <code>What type of water formation is formed by clouds?</code> | <code>beads of water are formed by water vapor condensing. Clouds are made of water vapor.. Beads of water can be formed by clouds.</code> | |
|
| <code>3LS2AMNW5FPNJK3C3PZLZCPX562OQO</code> | <code>Where do beads of water come from?</code> | <code>beads of water are formed by water vapor condensing. Condensation is the change of water vapor to a liquid.. Vapor turning into a liquid leaves behind beads of water</code> | |
|
| <code>3TMFV4NEP8DPIPCI8H9VUFHJG8V8W3</code> | <code>What forms beads of water? </code> | <code>beads of water are formed by water vapor condensing. An example of water vapor is steam.. Steam forms beads of water.</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "GISTEmbedLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
#### openbookqa_pairs |
|
|
|
* Dataset: [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa) at [388097e](https://huggingface.co/datasets/allenai/openbookqa/tree/388097ea7776314e93a529163e0fea805b8a6454) |
|
* Size: 2,740 training samples |
|
* Columns: <code>sentence1</code> and <code>sentence2</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | |
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 3 tokens</li><li>mean: 13.83 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.37 tokens</li><li>max: 30 tokens</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | |
|
|:-------------------------------------------------|:--------------------------------------------------------------------------| |
|
| <code>The sun is responsible for</code> | <code>the sun is the source of energy for physical cycles on Earth</code> | |
|
| <code>When food is reduced in the stomach</code> | <code>digestion is when stomach acid breaks down food</code> | |
|
| <code>Stars are</code> | <code>a star is made of gases</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "GISTEmbedLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
#### msmarco_pairs |
|
|
|
* Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) |
|
* Size: 12,500 training samples |
|
* Columns: <code>sentence1</code> and <code>sentence2</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | |
|
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 4 tokens</li><li>mean: 8.61 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 75.09 tokens</li><li>max: 206 tokens</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | |
|
|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>what are the liberal arts?</code> | <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code> | |
|
| <code>what is the mechanism of action of fibrinolytic or thrombolytic drugs?</code> | <code>Baillière's Clinical Haematology. 6 Mechanism of action of the thrombolytic agents. 6 Mechanism of action of the thrombolytic agents JEFFREY I. WEITZ Fibrin formed during the haemostatic, inflammatory or tissue repair process serves a temporary role, and must be degraded to restore normal tissue function and structure.</code> | |
|
| <code>what is normal plat count</code> | <code>78 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).The average platelet count is 237,000 per mcL in men and 266,000 per mcL in women.8 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "GISTEmbedLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
#### nq_pairs |
|
|
|
* Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) |
|
* Size: 12,500 training samples |
|
* Columns: <code>sentence1</code> and <code>sentence2</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | |
|
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 10 tokens</li><li>mean: 11.77 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 131.57 tokens</li><li>max: 512 tokens</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | |
|
|:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>when did richmond last play in a preliminary final</code> | <code>Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next.</code> | |
|
| <code>who sang what in the world's come over you</code> | <code>Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.</code> | |
|
| <code>who produces the most wool in the world</code> | <code>Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "GISTEmbedLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
#### trivia_pairs |
|
|
|
* Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) |
|
* Size: 12,500 training samples |
|
* Columns: <code>sentence1</code> and <code>sentence2</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | |
|
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 8 tokens</li><li>mean: 15.16 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 456.87 tokens</li><li>max: 512 tokens</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | |
|
|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Which American-born Sinclair won the Nobel Prize for Literature in 1930?</code> | <code>The Nobel Prize in Literature 1930 The Nobel Prize in Literature 1930 Sinclair Lewis The Nobel Prize in Literature 1930 Sinclair Lewis Prize share: 1/1 The Nobel Prize in Literature 1930 was awarded to Sinclair Lewis "for his vigorous and graphic art of description and his ability to create, with wit and humour, new types of characters". Photos: Copyright © The Nobel Foundation Share this: To cite this page MLA style: "The Nobel Prize in Literature 1930". Nobelprize.org. Nobel Media AB 2014. Web. 18 Jan 2017. <http://www.nobelprize.org/nobel_prizes/literature/laureates/1930/></code> | |
|
| <code>Where in England was Dame Judi Dench born?</code> | <code>Judi Dench - IMDb IMDb Actress | Music Department | Soundtrack Judi Dench was born in York, England, to Eleanora Olive (Jones), who was from Dublin, Ireland, and Reginald Arthur Dench, a doctor from Dorset, England. She attended Mount School in York, and studied at the Central School of Speech and Drama. She has performed with Royal Shakespeare Company, the National Theatre, and at Old Vic Theatre. She is a ... See full bio » Born: a list of 35 people created 02 Jul 2011 a list of 35 people created 19 Apr 2012 a list of 35 people created 28 May 2014 a list of 25 people created 05 Aug 2014 a list of 26 people created 18 May 2015 Do you have a demo reel? Add it to your IMDbPage How much of Judi Dench's work have you seen? User Polls Won 1 Oscar. Another 59 wins & 163 nominations. See more awards » Known For 2016 The Hollow Crown (TV Series) Cecily, Duchess of York 2015 The Vote (TV Movie) Christine Metcalfe - Total War (1996) ... Narrator (voice) - Stalemate (1996) ... Narrator (voice) 1992 The Torch (TV Mini-Series) Aba 1990 Screen One (TV Series) Anne 1989 Behaving Badly (TV Mini-Series) Bridget 1981 BBC2 Playhouse (TV Series) Sister Scarli 1976 Arena (TV Series documentary) Sweetie Simpkins 1973 Ooh La La! (TV Series) Amélie 1966 Court Martial (TV Series) Marthe 1963 Z Cars (TV Series) Elena Collins 1963 Love Story (TV Series) Pat McKendrick 1960 The Terrible Choice (TV Series) Good Angel Music department (1 credit) A Fine Romance (TV Series) (theme sung by - 14 episodes, 1981 - 1983) (theme song sung by - 12 episodes, 1983 - 1984) - A Romantic Meal (1984) ... (theme song sung by) - Problems (1984) ... (theme song sung by) 2013 Fifty Years on Stage (TV Movie) (performer: "Send in the Clowns") 2009 Nine (performer: "Folies Bergère") - What's Wrong with Mrs Bale? (1997) ... (performer: "Raindrops Keep Fallin' On My Head" - uncredited) - Misunderstandings (1993) ... (performer: "Walkin' My Baby Back Home" - uncredited) 1982-1984 A Fine Romance (TV Series) (performer - 2 episodes) - The Telephone Call (1984) ... (performer: "Boogie Woogie Bugle Boy" - uncredited) - Furniture (1982) ... (performer: "Rule, Britannia!" - uncredited) Hide 2009 Waiting in Rhyme (Video short) (special thanks) 2007 Expresso (Short) (special thanks) 1999 Shakespeare in Love and on Film (TV Movie documentary) (thanks - as Dame Judi Dench) Hide 2016 Rio Olympics (TV Mini-Series) Herself 2015 In Conversation (TV Series documentary) Herself 2015 Entertainment Tonight (TV Series) Herself 2015 CBS This Morning (TV Series) Herself - Guest 2015 The Insider (TV Series) Herself 1999-2014 Cinema 3 (TV Series) Herself 2013 Good Day L.A. (TV Series) Herself - Guest 2013 Arena (TV Series documentary) Herself 2013 At the Movies (TV Series) Herself 2013 Shooting Bond (Video documentary) Herself 2013 Bond's Greatest Moments (TV Movie documentary) Herself 2012 Made in Hollywood (TV Series) Herself 1999-2012 Charlie Rose (TV Series) Herself - Guest 2008-2012 This Morning (TV Series) Herself - Guest 2012 The Secrets of Skyfall (TV Short documentary) Herself 2012 Anderson Live (TV Series) Herself 2012 J. Edgar: A Complicated Man (Video documentary short) Herself 2011 The Many Faces of... (TV Series documentary) Herself / Various Characters 2011 Na plovárne (TV Series) Herself 2010 BBC Proms (TV Series) Herself 2010 The South Bank Show Revisited (TV Series documentary) Herself - Episode #6.68 (2009) ... Herself - Guest (as Dame Judi Dench) 2007-2009 Breakfast (TV Series) 2009 Larry King Live (TV Series) Herself - Guest 2009 The One Show (TV Series) Herself 2009 Cranford in Detail (Video documentary short) Herself / Miss Matty Jenkins (as Dame Judi Dench) 2005-2008 The South Bank Show (TV Series documentary) Herself 2008 Tavis Smiley (TV Series) Herself - Guest 2007 ITV News (TV Series) Herself - BAFTA Nominee 2007 The Making of Cranford (Video documentary short) Herself / Miss Matty Jenkyns (as Dame Judi Dench) 2006 Becoming Bond (TV Movie documentary) Herself 2006 Corazón de... (TV Series) Hers</code> | |
|
| <code>In which decade did Billboard magazine first publish and American hit chart?</code> | <code>The US Billboard song chart The US Billboard song chart Search this site with Google Song chart US Billboard The Billboard magazine has published various music charts starting (with sheet music) in 1894, the first "Music Hit Parade" was published in 1936 , the first "Music Popularity Chart" was calculated in 1940 . These charts became less irregular until the weekly "Hot 100" was started in 1958 . The current chart combines sales, airplay and downloads. A music collector that calls himself Bullfrog has been consolidating the complete chart from 1894 to the present day. he has published this information in a comprehenive spreadsheet (which can be obtained at bullfrogspond.com/ ). The Bullfrog data assigns each song a unique identifier, something like "1968_076" (which just happens to be the Bee Gees song "I've Gotta Get A Message To You"). This "Whitburn Number" is provided to match with the books of Joel Whitburn and consists of the year and a ranking within the year. A song that first entered the charts in December and has a long run is listed the following year. This numbering scheme means that songs which are still in the charts cannot be assigned a final id, because their ranking might change. So the definitive listing for a year cannot be final until about April. In our listing we only use songs with finalised IDs, this means that every year we have to wait until last year's entries are finalised before using them. (Source bullfrogspond.com/ , the original version used here was 20090808 with extra data from: the 2009 data from 20091219 the 2010 data from 20110305 the 2011 data from 20120929 the 2012 data from 20130330 the 2013 data from 20150328 The 20150328 data was the last one produced before the Billboard company forced the data to be withdrawn. As far as we know there are no more recent data sets available. This pattern of obtaining the data for a particular year in the middle of the following one comes from the way that the Bullfrog project generates the identifier for a song (what they call the "Prefix" in the spreadsheet). Recent entries are identified with keys like "2015-008" while older ones have keys like "2013_177". In the second case the underscore is significant, it indicates that this was the 177th biggest song released in 2013. Now, of course, during the year no one knows where a particular song will rank, so the underscore names can't be assigned until every song from a particular year has dropped out of the charts, so recent records are temporarily assigned a name with a dash. In about May of the following year the rankings are calculated and the final identifiers are assigned. That is why we at the Turret can only grab this data retrospectively. Attributes The original spreadsheet has a number of attributes, we have limited our attention to just a few of them: 134 9 The songs with the most entries on the chart were White Christmas (with 33 versions and a total of 110 weeks) and Stardust (with 19 and a total of 106 weeks). position The peak position that songs reached in the charts should show an smooth curve from number one down to the lowest position. This chart has more songs in the lower peak positions than one would expect. Before 1991 the profile of peak positions was exactly as you would expect, that year Billboard introduced the concept of "Recurrent" tracks, that is they removed any track from the chart which had spent more than twenty weeks in the chart and had fallen to the lower positions. weeks The effect of the "Recurrent" process, by which tracks are removed if they have spent at least twenty weeks in the chart and have fallen to the lower reaches, can clearly be seen in the strange spike in this attribute. This "adjustment" was intended to promote newer songs and ensure the chart does not become "stale". In fact since it was introduced in 1991 the length of long chart runs has increased, this might reflect the more conscious efforts of record companies to "game" the charts by controlling release times and promotions, or it coul</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "GISTEmbedLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
#### quora_pairs |
|
|
|
* Dataset: [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) |
|
* Size: 8,000 training samples |
|
* Columns: <code>sentence1</code> and <code>sentence2</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | |
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 6 tokens</li><li>mean: 13.53 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.68 tokens</li><li>max: 43 tokens</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | |
|
|:----------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| |
|
| <code>Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me?</code> | <code>I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me?</code> | |
|
| <code>How can I be a good geologist?</code> | <code>What should I do to be a great geologist?</code> | |
|
| <code>How do I read and find my YouTube comments?</code> | <code>How can I see all my Youtube comments?</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "GISTEmbedLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
#### gooaq_pairs |
|
|
|
* Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) |
|
* Size: 12,500 training samples |
|
* Columns: <code>sentence1</code> and <code>sentence2</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | |
|
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 8 tokens</li><li>mean: 11.6 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 57.74 tokens</li><li>max: 127 tokens</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | |
|
|:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>is toprol xl the same as metoprolol?</code> | <code>Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure.</code> | |
|
| <code>are you experienced cd steve hoffman?</code> | <code>The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design.</code> | |
|
| <code>how are babushka dolls made?</code> | <code>Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting.</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "GISTEmbedLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
### Evaluation Datasets |
|
|
|
#### nli-pairs |
|
|
|
* Dataset: [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
|
* Size: 1,500 evaluation samples |
|
* Columns: <code>anchor</code> and <code>positive</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | anchor | positive | |
|
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 5 tokens</li><li>mean: 17.64 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.67 tokens</li><li>max: 29 tokens</li></ul> | |
|
* Samples: |
|
| anchor | positive | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------| |
|
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | |
|
| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | |
|
| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "GISTEmbedLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
#### scitail-pairs-pos |
|
|
|
* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) |
|
* Size: 1,304 evaluation samples |
|
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | label | |
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| |
|
| type | string | string | int | |
|
| details | <ul><li>min: 5 tokens</li><li>mean: 22.52 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.34 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>0: ~47.50%</li><li>1: ~52.50%</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | label | |
|
|:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------| |
|
| <code>An introduction to atoms and elements, compounds, atomic structure and bonding, the molecule and chemical reactions.</code> | <code>Replace another in a molecule happens to atoms during a substitution reaction.</code> | <code>0</code> | |
|
| <code>Wavelength The distance between two consecutive points on a sinusoidal wave that are in phase;</code> | <code>Wavelength is the distance between two corresponding points of adjacent waves called.</code> | <code>1</code> | |
|
| <code>humans normally have 23 pairs of chromosomes.</code> | <code>Humans typically have 23 pairs pairs of chromosomes.</code> | <code>1</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "GISTEmbedLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
#### qnli-contrastive |
|
|
|
* Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) |
|
* Size: 1,500 evaluation samples |
|
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | label | |
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------| |
|
| type | string | string | int | |
|
| details | <ul><li>min: 6 tokens</li><li>mean: 14.13 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 36.58 tokens</li><li>max: 225 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | label | |
|
|:--------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
|
| <code>What came into force after the new constitution was herald?</code> | <code>As of that day, the new constitution heralding the Second Republic came into force.</code> | <code>0</code> | |
|
| <code>What is the first major city in the stream of the Rhine?</code> | <code>The most important tributaries in this area are the Ill below of Strasbourg, the Neckar in Mannheim and the Main across from Mainz.</code> | <code>0</code> | |
|
| <code>What is the minimum required if you want to teach in Canada?</code> | <code>In most provinces a second Bachelor's Degree such as a Bachelor of Education is required to become a qualified teacher.</code> | <code>0</code> | |
|
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "OnlineContrastiveLoss", |
|
"n_layers_per_step": -1, |
|
"last_layer_weight": 1, |
|
"prior_layers_weight": 0.5, |
|
"kl_div_weight": 1.25, |
|
"kl_temperature": 0.75 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: steps |
|
- `per_device_train_batch_size`: 30 |
|
- `per_device_eval_batch_size`: 24 |
|
- `learning_rate`: 3e-05 |
|
- `weight_decay`: 7.5e-07 |
|
- `lr_scheduler_type`: cosine_with_restarts |
|
- `lr_scheduler_kwargs`: {'num_cycles': 3} |
|
- `warmup_ratio`: 0.25 |
|
- `save_safetensors`: False |
|
- `fp16`: True |
|
- `push_to_hub`: True |
|
- `hub_model_id`: bobox/DeBERTaV3-TR-AllSoft-HT-n |
|
- `hub_strategy`: checkpoint |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: steps |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 30 |
|
- `per_device_eval_batch_size`: 24 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 1 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 3e-05 |
|
- `weight_decay`: 7.5e-07 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 3 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine_with_restarts |
|
- `lr_scheduler_kwargs`: {'num_cycles': 3} |
|
- `warmup_ratio`: 0.25 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: False |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: False |
|
- `fp16`: True |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: None |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: False |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: True |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: bobox/DeBERTaV3-TR-AllSoft-HT-n |
|
- `hub_strategy`: checkpoint |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
<details><summary>Click to expand</summary> |
|
|
|
| Epoch | Step | Training Loss | nli-pairs loss | scitail-pairs-pos loss | qnli-contrastive loss | sts-test_spearman_cosine | |
|
|:------:|:-----:|:-------------:|:--------------:|:----------------------:|:---------------------:|:------------------------:| |
|
| 0.0151 | 79 | 12.973 | - | - | - | - | |
|
| 0.0303 | 158 | 8.4661 | - | - | - | - | |
|
| 0.0454 | 237 | 6.136 | - | - | - | - | |
|
| 0.0605 | 316 | 6.2319 | - | - | - | - | |
|
| 0.0751 | 392 | - | 5.3137 | 3.9776 | 5.5203 | - | |
|
| 0.0756 | 395 | 5.6068 | - | - | - | - | |
|
| 0.0908 | 474 | 5.2502 | - | - | - | - | |
|
| 0.1059 | 553 | 4.8699 | - | - | - | - | |
|
| 0.1210 | 632 | 4.9584 | - | - | - | - | |
|
| 0.1361 | 711 | 4.5632 | - | - | - | - | |
|
| 0.1501 | 784 | - | 4.2657 | 2.4583 | 4.8120 | - | |
|
| 0.1513 | 790 | 4.223 | - | - | - | - | |
|
| 0.1664 | 869 | 3.8496 | - | - | - | - | |
|
| 0.1815 | 948 | 3.4414 | - | - | - | - | |
|
| 0.1966 | 1027 | 3.3513 | - | - | - | - | |
|
| 0.2118 | 1106 | 3.5611 | - | - | - | - | |
|
| 0.2252 | 1176 | - | 3.1783 | 1.9833 | 3.4508 | - | |
|
| 0.2269 | 1185 | 3.4039 | - | - | - | - | |
|
| 0.2420 | 1264 | 3.4269 | - | - | - | - | |
|
| 0.2571 | 1343 | 3.1573 | - | - | - | - | |
|
| 0.2723 | 1422 | 3.253 | - | - | - | - | |
|
| 0.2874 | 1501 | 2.7614 | - | - | - | - | |
|
| 0.3002 | 1568 | - | 2.7229 | 1.6435 | 2.9448 | - | |
|
| 0.3025 | 1580 | 2.9549 | - | - | - | - | |
|
| 0.3176 | 1659 | 2.8357 | - | - | - | - | |
|
| 0.3328 | 1738 | 2.8964 | - | - | - | - | |
|
| 0.3479 | 1817 | 2.8274 | - | - | - | - | |
|
| 0.3630 | 1896 | 2.6809 | - | - | - | - | |
|
| 0.3753 | 1960 | - | 2.4286 | 1.3531 | 2.4049 | - | |
|
| 0.3781 | 1975 | 2.3456 | - | - | - | - | |
|
| 0.3933 | 2054 | 2.5316 | - | - | - | - | |
|
| 0.4084 | 2133 | 2.653 | - | - | - | - | |
|
| 0.4235 | 2212 | 2.699 | - | - | - | - | |
|
| 0.4386 | 2291 | 2.424 | - | - | - | - | |
|
| 0.4503 | 2352 | - | 2.2071 | 1.2253 | 2.2926 | - | |
|
| 0.4538 | 2370 | 2.4716 | - | - | - | - | |
|
| 0.4689 | 2449 | 2.0097 | - | - | - | - | |
|
| 0.4840 | 2528 | 2.3993 | - | - | - | - | |
|
| 0.4991 | 2607 | 2.3295 | - | - | - | - | |
|
| 0.5143 | 2686 | 2.348 | - | - | - | - | |
|
| 0.5254 | 2744 | - | 2.0092 | 1.0735 | 2.0000 | - | |
|
| 0.5294 | 2765 | 2.0747 | - | - | - | - | |
|
| 0.5445 | 2844 | 2.3592 | - | - | - | - | |
|
| 0.5596 | 2923 | 2.2563 | - | - | - | - | |
|
| 0.5748 | 3002 | 2.3484 | - | - | - | - | |
|
| 0.5899 | 3081 | 1.868 | - | - | - | - | |
|
| 0.6004 | 3136 | - | 1.8467 | 0.9630 | 1.9593 | - | |
|
| 0.6050 | 3160 | 1.9958 | - | - | - | - | |
|
| 0.6201 | 3239 | 2.0089 | - | - | - | - | |
|
| 0.6353 | 3318 | 1.8303 | - | - | - | - | |
|
| 0.6504 | 3397 | 1.6892 | - | - | - | - | |
|
| 0.6655 | 3476 | 1.8379 | - | - | - | - | |
|
| 0.6755 | 3528 | - | 1.7487 | 0.9057 | 1.7077 | - | |
|
| 0.6806 | 3555 | 1.4958 | - | - | - | - | |
|
| 0.6958 | 3634 | 1.9504 | - | - | - | - | |
|
| 0.7109 | 3713 | 1.6017 | - | - | - | - | |
|
| 0.7260 | 3792 | 1.7229 | - | - | - | - | |
|
| 0.7411 | 3871 | 1.5996 | - | - | - | - | |
|
| 0.7505 | 3920 | - | 1.6036 | 0.7905 | 1.7369 | - | |
|
| 0.7563 | 3950 | 1.6257 | - | - | - | - | |
|
| 0.7714 | 4029 | 1.6094 | - | - | - | - | |
|
| 0.7865 | 4108 | 1.6061 | - | - | - | - | |
|
| 0.8016 | 4187 | 1.8917 | - | - | - | - | |
|
| 0.8168 | 4266 | 1.766 | - | - | - | - | |
|
| 0.8256 | 4312 | - | 1.5218 | 0.7310 | 1.8110 | - | |
|
| 0.8319 | 4345 | 1.6544 | - | - | - | - | |
|
| 0.8470 | 4424 | 1.5872 | - | - | - | - | |
|
| 0.8621 | 4503 | 1.6237 | - | - | - | - | |
|
| 0.8773 | 4582 | 1.5713 | - | - | - | - | |
|
| 0.8924 | 4661 | 1.5109 | - | - | - | - | |
|
| 0.9006 | 4704 | - | 1.4145 | 0.7044 | 1.5930 | - | |
|
| 0.9075 | 4740 | 1.8614 | - | - | - | - | |
|
| 0.9226 | 4819 | 1.2809 | - | - | - | - | |
|
| 0.9378 | 4898 | 1.4557 | - | - | - | - | |
|
| 0.9529 | 4977 | 2.285 | - | - | - | - | |
|
| 0.9680 | 5056 | 1.5918 | - | - | - | - | |
|
| 0.9757 | 5096 | - | 1.3623 | 0.6618 | 1.5226 | - | |
|
| 0.9832 | 5135 | 1.5956 | - | - | - | - | |
|
| 0.9983 | 5214 | 1.309 | - | - | - | - | |
|
| 1.0134 | 5293 | 1.6033 | - | - | - | - | |
|
| 1.0285 | 5372 | 1.2943 | - | - | - | - | |
|
| 1.0437 | 5451 | 1.4881 | - | - | - | - | |
|
| 1.0507 | 5488 | - | 1.3222 | 0.6280 | 1.5667 | - | |
|
| 1.0588 | 5530 | 1.6734 | - | - | - | - | |
|
| 1.0739 | 5609 | 1.6602 | - | - | - | - | |
|
| 1.0890 | 5688 | 1.4626 | - | - | - | - | |
|
| 1.1042 | 5767 | 1.4048 | - | - | - | - | |
|
| 1.1193 | 5846 | 1.5961 | - | - | - | - | |
|
| 1.1258 | 5880 | - | 1.2571 | 0.6172 | 1.4182 | - | |
|
| 1.1344 | 5925 | 1.4949 | - | - | - | - | |
|
| 1.1495 | 6004 | 1.7542 | - | - | - | - | |
|
| 1.1647 | 6083 | 1.3177 | - | - | - | - | |
|
| 1.1798 | 6162 | 1.1522 | - | - | - | - | |
|
| 1.1949 | 6241 | 1.0727 | - | - | - | - | |
|
| 1.2008 | 6272 | - | 1.2002 | 0.5877 | 1.5080 | - | |
|
| 1.2100 | 6320 | 1.598 | - | - | - | - | |
|
| 1.2252 | 6399 | 1.3773 | - | - | - | - | |
|
| 1.2403 | 6478 | 1.6495 | - | - | - | - | |
|
| 1.2554 | 6557 | 1.3042 | - | - | - | - | |
|
| 1.2705 | 6636 | 1.5158 | - | - | - | - | |
|
| 1.2759 | 6664 | - | 1.1646 | 0.5752 | 1.3319 | - | |
|
| 1.2857 | 6715 | 1.2205 | - | - | - | - | |
|
| 1.3008 | 6794 | 1.3909 | - | - | - | - | |
|
| 1.3159 | 6873 | 1.4255 | - | - | - | - | |
|
| 1.3310 | 6952 | 1.5415 | - | - | - | - | |
|
| 1.3462 | 7031 | 1.3355 | - | - | - | - | |
|
| 1.3509 | 7056 | - | 1.1401 | 0.5660 | 1.2625 | - | |
|
| 1.3613 | 7110 | 1.5326 | - | - | - | - | |
|
| 1.3764 | 7189 | 1.0109 | - | - | - | - | |
|
| 1.3915 | 7268 | 1.2729 | - | - | - | - | |
|
| 1.4067 | 7347 | 1.605 | - | - | - | - | |
|
| 1.4218 | 7426 | 1.4983 | - | - | - | - | |
|
| 1.4260 | 7448 | - | 1.1366 | 0.5671 | 1.2691 | - | |
|
| 1.4369 | 7505 | 1.2901 | - | - | - | - | |
|
| 1.4520 | 7584 | 1.4993 | - | - | - | - | |
|
| 1.4672 | 7663 | 1.0473 | - | - | - | - | |
|
| 1.4823 | 7742 | 1.2113 | - | - | - | - | |
|
| 1.4974 | 7821 | 1.3604 | - | - | - | - | |
|
| 1.5011 | 7840 | - | 1.1347 | 0.5652 | 1.2611 | - | |
|
| 1.5125 | 7900 | 1.4627 | - | - | - | - | |
|
| 1.5277 | 7979 | 1.1015 | - | - | - | - | |
|
| 1.5428 | 8058 | 1.4538 | - | - | - | - | |
|
| 1.5579 | 8137 | 1.4412 | - | - | - | - | |
|
| 1.5730 | 8216 | 1.4793 | - | - | - | - | |
|
| 1.5761 | 8232 | - | 1.1918 | 0.5849 | 1.4695 | - | |
|
| 1.5882 | 8295 | 1.1267 | - | - | - | - | |
|
| 1.6033 | 8374 | 1.1734 | - | - | - | - | |
|
| 1.6184 | 8453 | 1.2193 | - | - | - | - | |
|
| 1.6335 | 8532 | 1.1381 | - | - | - | - | |
|
| 1.6487 | 8611 | 0.9632 | - | - | - | - | |
|
| 1.6512 | 8624 | - | 1.1100 | 0.5500 | 1.2090 | - | |
|
| 1.6638 | 8690 | 1.0842 | - | - | - | - | |
|
| 1.6789 | 8769 | 0.7981 | - | - | - | - | |
|
| 1.6940 | 8848 | 1.2196 | - | - | - | - | |
|
| 1.7092 | 8927 | 0.8321 | - | - | - | - | |
|
| 1.7243 | 9006 | 0.938 | - | - | - | - | |
|
| 1.7262 | 9016 | - | 1.1021 | 0.5197 | 1.3204 | - | |
|
| 1.7394 | 9085 | 1.0008 | - | - | - | - | |
|
| 1.7545 | 9164 | 0.8644 | - | - | - | - | |
|
| 1.7697 | 9243 | 1.0459 | - | - | - | - | |
|
| 1.7848 | 9322 | 0.9344 | - | - | - | - | |
|
| 1.7999 | 9401 | 1.0636 | - | - | - | - | |
|
| 1.8013 | 9408 | - | 1.0662 | 0.5188 | 0.9692 | - | |
|
| 1.8150 | 9480 | 1.2482 | - | - | - | - | |
|
| 1.8302 | 9559 | 1.0134 | - | - | - | - | |
|
| 1.8453 | 9638 | 0.981 | - | - | - | - | |
|
| 1.8604 | 9717 | 1.0289 | - | - | - | - | |
|
| 1.8756 | 9796 | 0.9656 | - | - | - | - | |
|
| 1.8763 | 9800 | - | 1.0254 | 0.4962 | 1.0921 | - | |
|
| 1.8907 | 9875 | 0.9088 | - | - | - | - | |
|
| 1.9058 | 9954 | 1.2097 | - | - | - | - | |
|
| 1.9209 | 10033 | 0.7741 | - | - | - | - | |
|
| 1.9361 | 10112 | 0.8206 | - | - | - | - | |
|
| 1.9512 | 10191 | 0.8686 | - | - | - | - | |
|
| 1.9514 | 10192 | - | 1.0038 | 0.4778 | 0.9486 | - | |
|
| 1.9663 | 10270 | 0.7649 | - | - | - | - | |
|
| 1.9814 | 10349 | 0.9249 | - | - | - | - | |
|
| 1.9966 | 10428 | 0.6997 | - | - | - | - | |
|
| 2.0117 | 10507 | 1.06 | - | - | - | - | |
|
| 2.0264 | 10584 | - | 1.0006 | 0.4818 | 0.9664 | - | |
|
| 2.0268 | 10586 | 0.9447 | - | - | - | - | |
|
| 2.0419 | 10665 | 1.0151 | - | - | - | - | |
|
| 2.0571 | 10744 | 1.113 | - | - | - | - | |
|
| 2.0722 | 10823 | 1.1183 | - | - | - | - | |
|
| 2.0873 | 10902 | 1.1639 | - | - | - | - | |
|
| 2.1015 | 10976 | - | 0.9944 | 0.4786 | 0.9332 | - | |
|
| 2.1024 | 10981 | 1.0222 | - | - | - | - | |
|
| 2.1176 | 11060 | 1.244 | - | - | - | - | |
|
| 2.1327 | 11139 | 1.0128 | - | - | - | - | |
|
| 2.1478 | 11218 | 1.3783 | - | - | - | - | |
|
| 2.1629 | 11297 | 1.0301 | - | - | - | - | |
|
| 2.1765 | 11368 | - | 0.9803 | 0.4751 | 0.9649 | - | |
|
| 2.1781 | 11376 | 0.8699 | - | - | - | - | |
|
| 2.1932 | 11455 | 0.7565 | - | - | - | - | |
|
| 2.2083 | 11534 | 1.3038 | - | - | - | - | |
|
| 2.2234 | 11613 | 0.9584 | - | - | - | - | |
|
| 2.2386 | 11692 | 1.4689 | - | - | - | - | |
|
| 2.2516 | 11760 | - | 0.9802 | 0.4722 | 0.9584 | - | |
|
| 2.2537 | 11771 | 0.9979 | - | - | - | - | |
|
| 2.2688 | 11850 | 1.3444 | - | - | - | - | |
|
| 2.2839 | 11929 | 1.2052 | - | - | - | - | |
|
| 2.2991 | 12008 | 1.2007 | - | - | - | - | |
|
| 2.3142 | 12087 | 1.1402 | - | - | - | - | |
|
| 2.3266 | 12152 | - | 1.0053 | 0.4767 | 1.2373 | - | |
|
| 2.3293 | 12166 | 1.5263 | - | - | - | - | |
|
| 2.3444 | 12245 | 1.263 | - | - | - | - | |
|
| 2.3596 | 12324 | 1.1912 | - | - | - | - | |
|
| 2.3747 | 12403 | 1.0982 | - | - | - | - | |
|
| 2.3898 | 12482 | 1.1574 | - | - | - | - | |
|
| 2.4017 | 12544 | - | 0.9759 | 0.4915 | 1.1089 | - | |
|
| 2.4049 | 12561 | 1.4077 | - | - | - | - | |
|
| 2.4201 | 12640 | 1.3183 | - | - | - | - | |
|
| 2.4352 | 12719 | 1.0883 | - | - | - | - | |
|
| 2.4503 | 12798 | 1.3182 | - | - | - | - | |
|
| 2.4654 | 12877 | 1.0089 | - | - | - | - | |
|
| 2.4767 | 12936 | - | 0.9482 | 0.4552 | 1.1651 | - | |
|
| 2.4806 | 12956 | 1.0616 | - | - | - | - | |
|
| 2.4957 | 13035 | 1.1083 | - | - | - | - | |
|
| 2.5108 | 13114 | 1.2687 | - | - | - | - | |
|
| 2.5259 | 13193 | 0.698 | - | - | - | - | |
|
| 2.5411 | 13272 | 1.0596 | - | - | - | - | |
|
| 2.5518 | 13328 | - | 0.9386 | 0.4552 | 1.0533 | - | |
|
| 2.5562 | 13351 | 1.1182 | - | - | - | - | |
|
| 2.5713 | 13430 | 0.9092 | - | - | - | - | |
|
| 2.5864 | 13509 | 0.8628 | - | - | - | - | |
|
| 2.6016 | 13588 | 0.762 | - | - | - | - | |
|
| 2.6167 | 13667 | 0.9521 | - | - | - | - | |
|
| 2.6268 | 13720 | - | 0.8890 | 0.4524 | 0.7946 | - | |
|
| 2.6318 | 13746 | 0.8631 | - | - | - | - | |
|
| 2.6469 | 13825 | 0.6899 | - | - | - | - | |
|
| 2.6621 | 13904 | 0.6552 | - | - | - | - | |
|
| 2.6772 | 13983 | 0.572 | - | - | - | - | |
|
| 2.6923 | 14062 | 0.9809 | - | - | - | - | |
|
| 2.7019 | 14112 | - | 0.8913 | 0.4352 | 0.7276 | - | |
|
| 2.7074 | 14141 | 0.5392 | - | - | - | - | |
|
| 2.7226 | 14220 | 0.6777 | - | - | - | - | |
|
| 2.7377 | 14299 | 0.6523 | - | - | - | - | |
|
| 2.7528 | 14378 | 0.6416 | - | - | - | - | |
|
| 2.7679 | 14457 | 0.7713 | - | - | - | - | |
|
| 2.7769 | 14504 | - | 0.8726 | 0.4271 | 0.7923 | - | |
|
| 2.7831 | 14536 | 0.6531 | - | - | - | - | |
|
| 2.7982 | 14615 | 0.7056 | - | - | - | - | |
|
| 2.8133 | 14694 | 1.054 | - | - | - | - | |
|
| 2.8285 | 14773 | 0.8535 | - | - | - | - | |
|
| 2.8436 | 14852 | 0.7321 | - | - | - | - | |
|
| 2.8520 | 14896 | - | 0.8662 | 0.4211 | 0.7694 | - | |
|
| 2.8587 | 14931 | 0.8236 | - | - | - | - | |
|
| 2.8738 | 15010 | 0.776 | - | - | - | - | |
|
| 2.8890 | 15089 | 0.7049 | - | - | - | - | |
|
| 2.9041 | 15168 | 0.9409 | - | - | - | - | |
|
| 2.9192 | 15247 | 0.7416 | - | - | - | - | |
|
| 2.9271 | 15288 | - | 0.8610 | 0.4205 | 0.7352 | - | |
|
| 2.9343 | 15326 | 0.6059 | - | - | - | - | |
|
| 2.9495 | 15405 | 0.6598 | - | - | - | - | |
|
| 2.9646 | 15484 | 0.5777 | - | - | - | - | |
|
| 2.9797 | 15563 | 0.8212 | - | - | - | - | |
|
| 2.9948 | 15642 | 0.5638 | - | - | - | - | |
|
| 3.0 | 15669 | - | - | - | - | 0.7818 | |
|
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</details> |
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### Framework Versions |
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- Python: 3.10.13 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.1.2 |
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- Accelerate: 0.30.1 |
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- Datasets: 2.19.2 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### AdaptiveLayerLoss |
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```bibtex |
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@misc{li20242d, |
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title={2D Matryoshka Sentence Embeddings}, |
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author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, |
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year={2024}, |
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eprint={2402.14776}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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#### CoSENTLoss |
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```bibtex |
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@online{kexuefm-8847, |
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title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
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author={Su Jianlin}, |
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year={2022}, |
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month={Jan}, |
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url={https://kexue.fm/archives/8847}, |
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} |
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``` |
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#### GISTEmbedLoss |
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```bibtex |
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@misc{solatorio2024gistembed, |
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title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, |
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author={Aivin V. Solatorio}, |
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year={2024}, |
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eprint={2402.16829}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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