--- base_model: BAAI/bge-base-en-v1.5 datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_ndcg@100 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10000 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Enzalutamide ( brand name Xtandi ) is a synthetic non-steroidal antiandrogen ( NSAA ) which was developed by the pharmaceutical company Medivation for the treatment of metastatic , castration-resistant prostate cancer . Medivation has reported up to an 89 % decrease in serum prostate specific antigen ( PSA ) levels after a month of taking the drug . Research suggests that enzalutamide may also be effective in the treatment of certain types of breast cancer . In August 2012 , the United States ( U.S. ) Food and Drug Administration ( FDA ) approved enzalutamide for the treatment of castration-resistant prostate cancer . sentences: - what type of cancer is enzalutamide - who is simon cho - who is dr william farone - source_sentence: Sohel Rana is a Bangladeshi footballer who plays as a midfielder . He currently plays for Sheikh Jamal Dhanmondi Club . sentences: - who is sohel rana - who is olympicos - who is roberto laserna - source_sentence: Qarah Qayeh ( قره قيه , also Romanized as Qareh Qīyeh ) is a village in Chaharduli Rural District , Keshavarz District , Shahin Dezh County , West Azerbaijan Province , Iran . At the 2006 census , its population was 465 , in 93 families . sentences: - what was the knoxville riot - what language is kbif - where is qarah qayeh - source_sentence: Martin Severin Janus From ( 8 April 1828 -- 6 May 1895 ) was a Danish chess master . Born in Nakskov , From received his first education at the grammar school of Nykøbing Falster . He entered the army as a volunteer during the Prussian-Danish War ( Schleswig-Holstein War of Succession ) , where he served in the brigade of Major-General Olaf Rye and partook in the Battle of Fredericia on July 6 , 1849 . After the war From settled in Copenhagen . He was employed by the Statistical Bureau , where he met Magnus Oscar Møllerstrøm , then the strongest chess player in Copenhagen . Next , he worked in the central office for prison management , and in 1890 he became an inspector of the penitentiary of Christianshavn . In 1891 he received the order Ridder af Dannebrog ( `` Knight of the Danish cloth '' , i.e. flag of Denmark ) , which is the second highest of Danish orders . In 1895 Severin From died of cancer . He is interred at Vestre Cemetery , Copenhagen . sentences: - when did martin from die - what is hymenoxys lemmonii - where is macomb square il - source_sentence: The Recession of 1937 -- 1938 was an economic downturn that occurred during the Great Depression in the United States . By the spring of 1937 , production , profits , and wages had regained their 1929 levels . Unemployment remained high , but it was slightly lower than the 25 % rate seen in 1933 . The American economy took a sharp downturn in mid-1937 , lasting for 13 months through most of 1938 . Industrial production declined almost 30 percent and production of durable goods fell even faster . Unemployment jumped from 14.3 % in 1937 to 19.0 % in 1938 . Manufacturing output fell by 37 % from the 1937 peak and was back to 1934 levels . Producers reduced their expenditures on durable goods , and inventories declined , but personal income was only 15 % lower than it had been at the peak in 1937 . In most sectors , hourly earnings continued to rise throughout the recession , which partly compensated for the reduction in the number of hours worked . As unemployment rose , consumers expenditures declined , thereby leading to further cutbacks in production . sentences: - when did the great depression peak in the u.s. economy? - what is tom mount's specialty - where is poulton model-index: - name: SentenceTransformer based on BAAI/bge-base-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.906 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.954 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.962 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.975 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.906 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.31799999999999995 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19240000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09750000000000003 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.906 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.954 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.962 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.975 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9422297521305668 name: Cosine Ndcg@10 - type: cosine_ndcg@100 value: 0.9458947974911144 name: Cosine Ndcg@100 - type: cosine_mrr@10 value: 0.9315763888888889 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9323383888065935 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("MugheesAwan11/bge-base-climate_fever-dataset-10k-2k-e2") # Run inference sentences = [ 'The Recession of 1937 -- 1938 was an economic downturn that occurred during the Great Depression in the United States . By the spring of 1937 , production , profits , and wages had regained their 1929 levels . Unemployment remained high , but it was slightly lower than the 25 % rate seen in 1933 . The American economy took a sharp downturn in mid-1937 , lasting for 13 months through most of 1938 . Industrial production declined almost 30 percent and production of durable goods fell even faster . Unemployment jumped from 14.3 % in 1937 to 19.0 % in 1938 . Manufacturing output fell by 37 % from the 1937 peak and was back to 1934 levels . Producers reduced their expenditures on durable goods , and inventories declined , but personal income was only 15 % lower than it had been at the peak in 1937 . In most sectors , hourly earnings continued to rise throughout the recession , which partly compensated for the reduction in the number of hours worked . As unemployment rose , consumers expenditures declined , thereby leading to further cutbacks in production .', 'when did the great depression peak in the u.s. economy?', 'where is poulton', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.906 | | cosine_accuracy@3 | 0.954 | | cosine_accuracy@5 | 0.962 | | cosine_accuracy@10 | 0.975 | | cosine_precision@1 | 0.906 | | cosine_precision@3 | 0.318 | | cosine_precision@5 | 0.1924 | | cosine_precision@10 | 0.0975 | | cosine_recall@1 | 0.906 | | cosine_recall@3 | 0.954 | | cosine_recall@5 | 0.962 | | cosine_recall@10 | 0.975 | | cosine_ndcg@10 | 0.9422 | | cosine_ndcg@100 | 0.9459 | | cosine_mrr@10 | 0.9316 | | **cosine_map@100** | **0.9323** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,000 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------| | Professor Maurice Cockrill , RA , FBA ( 8 October 1936 -- 1 December 2013 ) was a British painter and poet . Born in Hartlepool , County Durham , he studied at Wrexham School of Art , north east Wales , then Denbigh Technical College and later the University of Reading from 1960 -- 64 . In Liverpool , where he lived for nearly twenty years from 1964 , he taught at Liverpool College of Art and Liverpool Polytechnic . He was a central figure in Liverpool 's artistic life , regularly exhibiting at the Walker Art Gallery , before his departure for London in 1982 . Cockrill 's Liverpool work was in line with that of John Baum , Sam Walsh and Adrian Henri , employing Pop and Photo-Realist styles , but later he moved towards Romantic Expressionism , as it was shown in his retrospective at the Walker Art Gallery , Liverpool in 1995 . His poetry was published in magazines such as `` Ambit '' and `` Poetry Review '' . He was formerly the Keeper of the Royal Academy , and as such managed the RA Schools of the Establishment as well as being a member of the Board and Executive Committee . | who was maurice cockrill | | Nowa Dąbrowa -LSB- ` nowa-dom ` browa -RSB- is a village in the administrative district of Gmina Kwilcz , within Międzychód County , Greater Poland Voivodeship , in west-central Poland . It lies approximately 16 km south-east of Międzychód and 59 km west of the regional capital Poznań . The village has a population of 40 . | where is nowa dbrowa poland | | Hymenoxys lemmonii is a species of flowering plant in the daisy family known by the common names Lemmon 's rubberweed , Lemmon 's bitterweed , and alkali hymenoxys . It is native to the western United States in and around the Great Basin in Utah , Nevada , northern California , and southeastern Oregon . Hymenoxys lemmonii is a biennial or perennial herb with one or more branching stems growing erect to a maximum height near 50 centimeters ( 20 inches ) . It produces straight , dark green leaves up to 9 centimeters ( 3.6 inches ) long and divided into a number of narrow , pointed lobes . The foliage and stem may be hairless to quite woolly . The daisy-like flower head is generally at least 1.5 centimeters ( 0.6 inches ) wide , with a center of 50 -- 125 thick golden disc florets and a shaggy fringe of 9 -- 12 golden ray florets . The species is named for John Gill Lemmon , husband of prominent American botanist Sarah Plummer Lemmon . | what is hymenoxys lemmonii | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768 ], "matryoshka_weights": [ 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 2 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `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`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `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`: True - `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_fused - `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`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `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
### Training Logs | Epoch | Step | Training Loss | dim_768_cosine_map@100 | |:-------:|:-------:|:-------------:|:----------------------:| | 0.0319 | 10 | 0.1626 | - | | 0.0639 | 20 | 0.1168 | - | | 0.0958 | 30 | 0.0543 | - | | 0.1278 | 40 | 0.1227 | - | | 0.1597 | 50 | 0.061 | - | | 0.1917 | 60 | 0.0537 | - | | 0.2236 | 70 | 0.0693 | - | | 0.2556 | 80 | 0.1115 | - | | 0.2875 | 90 | 0.0541 | - | | 0.3195 | 100 | 0.0774 | - | | 0.3514 | 110 | 0.0639 | - | | 0.3834 | 120 | 0.0639 | - | | 0.4153 | 130 | 0.0567 | - | | 0.4473 | 140 | 0.0385 | - | | 0.4792 | 150 | 0.0452 | - | | 0.5112 | 160 | 0.0641 | - | | 0.5431 | 170 | 0.042 | - | | 0.5751 | 180 | 0.0243 | - | | 0.6070 | 190 | 0.0405 | - | | 0.6390 | 200 | 0.062 | - | | 0.6709 | 210 | 0.0366 | - | | 0.7029 | 220 | 0.0399 | - | | 0.7348 | 230 | 0.0382 | - | | 0.7668 | 240 | 0.0387 | - | | 0.7987 | 250 | 0.0575 | - | | 0.8307 | 260 | 0.0391 | - | | 0.8626 | 270 | 0.0776 | - | | 0.8946 | 280 | 0.0258 | - | | 0.9265 | 290 | 0.0493 | - | | 0.9585 | 300 | 0.037 | - | | 0.9904 | 310 | 0.0499 | - | | **1.0** | **313** | **-** | **0.9397** | | 0.0319 | 10 | 0.0111 | - | | 0.0639 | 20 | 0.007 | - | | 0.0958 | 30 | 0.0023 | - | | 0.1278 | 40 | 0.0109 | - | | 0.1597 | 50 | 0.0046 | - | | 0.1917 | 60 | 0.0043 | - | | 0.2236 | 70 | 0.0037 | - | | 0.2556 | 80 | 0.0118 | - | | 0.2875 | 90 | 0.0026 | - | | 0.3195 | 100 | 0.0079 | - | | 0.3514 | 110 | 0.0045 | - | | 0.3834 | 120 | 0.0163 | - | | 0.4153 | 130 | 0.0058 | - | | 0.4473 | 140 | 0.0154 | - | | 0.4792 | 150 | 0.0051 | - | | 0.5112 | 160 | 0.0152 | - | | 0.5431 | 170 | 0.0058 | - | | 0.5751 | 180 | 0.0041 | - | | 0.6070 | 190 | 0.0118 | - | | 0.6390 | 200 | 0.0165 | - | | 0.6709 | 210 | 0.0088 | - | | 0.7029 | 220 | 0.014 | - | | 0.7348 | 230 | 0.0195 | - | | 0.7668 | 240 | 0.024 | - | | 0.7987 | 250 | 0.0472 | - | | 0.8307 | 260 | 0.0341 | - | | 0.8626 | 270 | 0.0684 | - | | 0.8946 | 280 | 0.0193 | - | | 0.9265 | 290 | 0.0488 | - | | 0.9585 | 300 | 0.0388 | - | | 0.9904 | 310 | 0.0485 | - | | **1.0** | **313** | **-** | **0.9349** | | 1.0224 | 320 | 0.0119 | - | | 1.0543 | 330 | 0.013 | - | | 1.0863 | 340 | 0.0024 | - | | 1.1182 | 350 | 0.012 | - | | 1.1502 | 360 | 0.0042 | - | | 1.1821 | 370 | 0.0091 | - | | 1.2141 | 380 | 0.0041 | - | | 1.2460 | 390 | 0.0096 | - | | 1.2780 | 400 | 0.0053 | - | | 1.3099 | 410 | 0.0043 | - | | 1.3419 | 420 | 0.0059 | - | | 1.3738 | 430 | 0.0138 | - | | 1.4058 | 440 | 0.0132 | - | | 1.4377 | 450 | 0.0124 | - | | 1.4696 | 460 | 0.0049 | - | | 1.5016 | 470 | 0.0043 | - | | 1.5335 | 480 | 0.0045 | - | | 1.5655 | 490 | 0.0037 | - | | 1.5974 | 500 | 0.0081 | - | | 1.6294 | 510 | 0.0038 | - | | 1.6613 | 520 | 0.0055 | - | | 1.6933 | 530 | 0.003 | - | | 1.7252 | 540 | 0.0022 | - | | 1.7572 | 550 | 0.0042 | - | | 1.7891 | 560 | 0.0158 | - | | 1.8211 | 570 | 0.0088 | - | | 1.8530 | 580 | 0.0154 | - | | 1.8850 | 590 | 0.0057 | - | | 1.9169 | 600 | 0.0086 | - | | 1.9489 | 610 | 0.0069 | - | | 1.9808 | 620 | 0.0076 | - | | 2.0 | 626 | - | 0.9323 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.31.0 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```