|
--- |
|
base_model: BAAI/bge-base-en-v1.5 |
|
datasets: [] |
|
language: [] |
|
library_name: sentence-transformers |
|
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_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:6300 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: As of January 31, 2023, the weighted average remaining lease term |
|
for operating leases was 7 years and for finance leases was 3 years. |
|
sentences: |
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- What was the Company's net deferred tax assets as of December 30, 2023, and December |
|
31, 2022? |
|
- What were the weighted average remaining lease terms for operating and finance |
|
leases as of January 31, 2023? |
|
- How much did the net investment income change from 2021 to 2023? |
|
- source_sentence: The 4.500% notes due in August 2034 have an interest rate of 4.55%. |
|
sentences: |
|
- What types of insurance coverage does the company provide to its employees at |
|
no premium cost, as part of their general employee benefits package? |
|
- What is the interest rate for the 4.500% notes due in August 2034? |
|
- How much did the company's revenues decrease in 2023 compared to 2022? |
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- source_sentence: In 2023, other income (expense), net included $376 million of interest |
|
income, partially offset by $167 million of net unrealized losses on equity investments. |
|
Other income (expense), net in 2022 included $657 million of net unrealized losses |
|
on equity investments, partially offset by $106 million of interest income. |
|
sentences: |
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- What contributed to the net other income (expense) in 2023? |
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- What types of products does the Canada operation offer? |
|
- What was the net change in cash and cash equivalents in 2022? |
|
- source_sentence: We believe the claims in these cases are without merit and are |
|
vigorously defending these lawsuits. |
|
sentences: |
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- Where in the Annual Report can one find a description of certain legal matters |
|
and their impact on the company? |
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- What is the goal of the company regarding its global corporate operations by 2030? |
|
- What is the stance of the defending airlines on the claims made against them in |
|
the capacity antitrust litigation? |
|
- source_sentence: North America's total net revenues for the fiscal year ended October |
|
1, 2023, were $26,569.6 million. |
|
sentences: |
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- What was the total net revenue for North America in fiscal 2023? |
|
- What are the consequences of impermissible use or disclosure of PHI according |
|
to the HITECH Act? |
|
- What does the index in a financial report indicate? |
|
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: 0.6171428571428571 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7457142857142857 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8114285714285714 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8585714285714285 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6171428571428571 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.24857142857142858 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16228571428571428 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08585714285714285 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6171428571428571 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7457142857142857 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8114285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8585714285714285 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7357204832416036 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.6965260770975052 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7015509951793545 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6214285714285714 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.74 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8642857142857143 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6214285714285714 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.24666666666666665 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.15999999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08642857142857142 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6214285714285714 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.74 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8642857142857143 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.738181682287809 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.6983236961451246 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7027820040111107 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7271428571428571 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7928571428571428 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8442857142857143 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.24238095238095236 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.15857142857142856 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08442857142857142 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7271428571428571 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7928571428571428 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8442857142857143 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7182448637999702 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.6782879818594099 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.683606591058064 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.5728571428571428 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7014285714285714 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7557142857142857 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8157142857142857 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.5728571428571428 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2338095238095238 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1511428571428571 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08157142857142856 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.5728571428571428 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7014285714285714 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7557142857142857 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8157142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.6915163160852085 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.6521536281179136 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.6580414471513885 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.5142857142857142 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.6371428571428571 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6728571428571428 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7357142857142858 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.5142857142857142 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.21238095238095234 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13457142857142856 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07357142857142857 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.5142857142857142 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6371428571428571 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6728571428571428 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7357142857142858 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.6197107516374883 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.5832369614512468 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5907376271746598 |
|
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) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### 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("ethan-ky/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
|
"North America's total net revenues for the fiscal year ended October 1, 2023, were $26,569.6 million.", |
|
'What was the total net revenue for North America in fiscal 2023?', |
|
'What are the consequences of impermissible use or disclosure of PHI according to the HITECH Act?', |
|
] |
|
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] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_768` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.6171 | |
|
| cosine_accuracy@3 | 0.7457 | |
|
| cosine_accuracy@5 | 0.8114 | |
|
| cosine_accuracy@10 | 0.8586 | |
|
| cosine_precision@1 | 0.6171 | |
|
| cosine_precision@3 | 0.2486 | |
|
| cosine_precision@5 | 0.1623 | |
|
| cosine_precision@10 | 0.0859 | |
|
| cosine_recall@1 | 0.6171 | |
|
| cosine_recall@3 | 0.7457 | |
|
| cosine_recall@5 | 0.8114 | |
|
| cosine_recall@10 | 0.8586 | |
|
| cosine_ndcg@10 | 0.7357 | |
|
| cosine_mrr@10 | 0.6965 | |
|
| **cosine_map@100** | **0.7016** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_512` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.6214 | |
|
| cosine_accuracy@3 | 0.74 | |
|
| cosine_accuracy@5 | 0.8 | |
|
| cosine_accuracy@10 | 0.8643 | |
|
| cosine_precision@1 | 0.6214 | |
|
| cosine_precision@3 | 0.2467 | |
|
| cosine_precision@5 | 0.16 | |
|
| cosine_precision@10 | 0.0864 | |
|
| cosine_recall@1 | 0.6214 | |
|
| cosine_recall@3 | 0.74 | |
|
| cosine_recall@5 | 0.8 | |
|
| cosine_recall@10 | 0.8643 | |
|
| cosine_ndcg@10 | 0.7382 | |
|
| cosine_mrr@10 | 0.6983 | |
|
| **cosine_map@100** | **0.7028** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_256` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.6 | |
|
| cosine_accuracy@3 | 0.7271 | |
|
| cosine_accuracy@5 | 0.7929 | |
|
| cosine_accuracy@10 | 0.8443 | |
|
| cosine_precision@1 | 0.6 | |
|
| cosine_precision@3 | 0.2424 | |
|
| cosine_precision@5 | 0.1586 | |
|
| cosine_precision@10 | 0.0844 | |
|
| cosine_recall@1 | 0.6 | |
|
| cosine_recall@3 | 0.7271 | |
|
| cosine_recall@5 | 0.7929 | |
|
| cosine_recall@10 | 0.8443 | |
|
| cosine_ndcg@10 | 0.7182 | |
|
| cosine_mrr@10 | 0.6783 | |
|
| **cosine_map@100** | **0.6836** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_128` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:----------| |
|
| cosine_accuracy@1 | 0.5729 | |
|
| cosine_accuracy@3 | 0.7014 | |
|
| cosine_accuracy@5 | 0.7557 | |
|
| cosine_accuracy@10 | 0.8157 | |
|
| cosine_precision@1 | 0.5729 | |
|
| cosine_precision@3 | 0.2338 | |
|
| cosine_precision@5 | 0.1511 | |
|
| cosine_precision@10 | 0.0816 | |
|
| cosine_recall@1 | 0.5729 | |
|
| cosine_recall@3 | 0.7014 | |
|
| cosine_recall@5 | 0.7557 | |
|
| cosine_recall@10 | 0.8157 | |
|
| cosine_ndcg@10 | 0.6915 | |
|
| cosine_mrr@10 | 0.6522 | |
|
| **cosine_map@100** | **0.658** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.5143 | |
|
| cosine_accuracy@3 | 0.6371 | |
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| cosine_accuracy@5 | 0.6729 | |
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| cosine_accuracy@10 | 0.7357 | |
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| cosine_precision@1 | 0.5143 | |
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| cosine_precision@3 | 0.2124 | |
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| cosine_precision@5 | 0.1346 | |
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| cosine_precision@10 | 0.0736 | |
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| cosine_recall@1 | 0.5143 | |
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| cosine_recall@3 | 0.6371 | |
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| cosine_recall@5 | 0.6729 | |
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| cosine_recall@10 | 0.7357 | |
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| cosine_ndcg@10 | 0.6197 | |
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| cosine_mrr@10 | 0.5832 | |
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| **cosine_map@100** | **0.5907** | |
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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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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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|
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 6,300 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 2 tokens</li><li>mean: 45.35 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.67 tokens</li><li>max: 46 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
|
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------| |
|
| <code>Our ability to develop and operate units at the right locations and to deliver a customer-centric omni-channel experience largely determines our competitive position within the retail industry. We believe price leadership is a critical part of our business model and we continue to focus on moving our markets towards an EDLP approach. Additionally, our ability to operate food departments effectively has a significant impact on our competitive position in the markets where we operate.</code> | <code>What factors contribute to Walmart International's competitive position?</code> | |
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| <code>tax annual aggregate losses incurred in any year from U.S. hurricane events could be in excess of $3,827 million (or 6.4 percent of total Chubb shareholders’ equity at December 31, 2023).</code> | <code>What is the expected maximum potential loss from hurricane events for Chubb as of the end of 2023?</code> | |
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| <code>The 'Glossary of Terms and Acronyms’ is included on pages 315-321.</code> | <code>What is included on pages 315 to 321 of the document?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
|
|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
|
</details> |
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|
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### Training Logs |
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| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.8122 | 10 | 1.3939 | - | - | - | - | - | |
|
| **0.9746** | **12** | **-** | **0.658** | **0.6836** | **0.7028** | **0.5907** | **0.7016** | |
|
| 1.6244 | 20 | 1.3574 | - | - | - | - | - | |
|
| 1.9492 | 24 | - | 0.6580 | 0.6836 | 0.7028 | 0.5907 | 0.7016 | |
|
| 2.4365 | 30 | 1.3485 | - | - | - | - | - | |
|
| 2.9239 | 36 | - | 0.6580 | 0.6836 | 0.7028 | 0.5907 | 0.7016 | |
|
| 3.2487 | 40 | 1.3606 | - | - | - | - | - | |
|
| 3.8985 | 48 | - | 0.6580 | 0.6836 | 0.7028 | 0.5907 | 0.7016 | |
|
|
|
* The bold row denotes the saved checkpoint. |
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|
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### Framework Versions |
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- Python: 3.9.19 |
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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+cu121 |
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- Accelerate: 0.33.0 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.19.1 |
|
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## Citation |
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### BibTeX |
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|
#### Sentence Transformers |
|
```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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#### MatryoshkaLoss |
|
```bibtex |
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@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
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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}, |
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eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
|
``` |
|
|
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#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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primaryClass={cs.CL} |
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} |
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``` |
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<!-- |
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## Glossary |
|
|
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*Clearly define terms in order to be accessible across audiences.* |
|
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<!-- |
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## Model Card Authors |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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