|
--- |
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base_model: BAAI/bge-base-en-v1.5 |
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datasets: [] |
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language: |
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- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
|
- cosine_precision@3 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
|
- cosine_mrr@10 |
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- cosine_map@100 |
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pipeline_tag: sentence-similarity |
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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:6300 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: The table indicates that 18,000 deferred shares were granted to |
|
non-employee directors in fiscal 2020, 15,000 in fiscal 2021, and 19,000 in fiscal |
|
2022. |
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sentences: |
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- What was the primary reason for the increased audit effort for PCC goodwill and |
|
indefinite-lived intangible assets? |
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- How many deferred shares were granted to non-employee directors in fiscal 2020, |
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2021, and 2022? |
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- What was the total intrinsic value of options exercised in fiscal year 2023? |
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- source_sentence: In Resource Masking Industries, we expect the profit impact from |
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lower sales volume to be partially offset by favorable price realization. |
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sentences: |
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- By what percentage did Electronic Arts' operating income grow in the fiscal year |
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ended March 31, 2023? |
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- What impact is expected on Resource Industries' profit due to lower sales volume? |
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- How are IBM’s 2023 Annual Report to Stockholders' financial statements made a |
|
part of Form 10-K? |
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- source_sentence: The actuarial gain during the year ended December 31, 2022 was |
|
primarily related to increases in the discount rate assumptions. |
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sentences: |
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- What was the primary reason for the actuarial gain during the year ended December |
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31, 2022? |
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- How much did Ford's total assets amount to by December 31, 2023? |
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- What was the remaining available amount of the share repurchase authorization |
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as of January 29, 2023? |
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- source_sentence: Returned $1.7 billion to shareholders through share repurchases |
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and dividend payments. |
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sentences: |
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- What was the carrying amount of investments without readily determinable fair |
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values as of December 31, 2023? |
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- What are the significant inputs to the valuation of Goldman Sachs' unsecured short- |
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and long-term borrowings? |
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- How much did the company return to shareholders through share repurchases and |
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dividend payments in 2022? |
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- source_sentence: The remaining amount available for borrowing under the Revolving |
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Credit Facility as of December 31, 2023, was $2,245.2 million. |
|
sentences: |
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- What was the total amount available for borrowing under the Revolving Credit Facility |
|
at Iron Mountain as of December 31, 2023? |
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- What type of information is included in Note 13 of the Annual Report on Form 10-K? |
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- How much did local currency revenue increase in Latin America in 2023 compared |
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to 2022? |
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model-index: |
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- name: BGE base Financial Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
|
value: 0.6828571428571428 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8242857142857143 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8557142857142858 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9057142857142857 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6828571428571428 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2747619047619047 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17114285714285712 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09057142857142855 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6828571428571428 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8242857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8557142857142858 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9057142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7963610970343802 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7612930839002267 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
|
value: 0.7648513048205645 |
|
name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
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name: dim 512 |
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type: dim_512 |
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metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.68 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8157142857142857 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8542857142857143 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.68 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.27190476190476187 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17085714285714285 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.68 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8157142857142857 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8542857142857143 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7911616934987842 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7562284580498863 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.760087172570928 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.68 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8114285714285714 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8485714285714285 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8971428571428571 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.68 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2704761904761905 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16971428571428568 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0897142857142857 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.68 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8114285714285714 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8485714285714285 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8971428571428571 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7888581850866868 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7542278911564625 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7579536807505182 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6571428571428571 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.79 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8285714285714286 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8857142857142857 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6571428571428571 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2633333333333333 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1657142857142857 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08857142857142856 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6571428571428571 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.79 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8285714285714286 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8857142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7703812626851927 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.733632653061224 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7378840513025602 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.62 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.77 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8028571428571428 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.85 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.62 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.25666666666666665 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16057142857142856 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.085 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.62 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.77 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8028571428571428 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.85 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.73777886683529 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7016190476190474 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7073607864232172 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE base Financial Matryoshka |
|
|
|
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:** 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("moritzglnr/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
|
'The remaining amount available for borrowing under the Revolving Credit Facility as of December 31, 2023, was $2,245.2 million.', |
|
'What was the total amount available for borrowing under the Revolving Credit Facility at Iron Mountain as of December 31, 2023?', |
|
'What type of information is included in Note 13 of the Annual Report on Form 10-K?', |
|
] |
|
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 | 0.6829 | |
|
| cosine_accuracy@3 | 0.8243 | |
|
| cosine_accuracy@5 | 0.8557 | |
|
| cosine_accuracy@10 | 0.9057 | |
|
| cosine_precision@1 | 0.6829 | |
|
| cosine_precision@3 | 0.2748 | |
|
| cosine_precision@5 | 0.1711 | |
|
| cosine_precision@10 | 0.0906 | |
|
| cosine_recall@1 | 0.6829 | |
|
| cosine_recall@3 | 0.8243 | |
|
| cosine_recall@5 | 0.8557 | |
|
| cosine_recall@10 | 0.9057 | |
|
| cosine_ndcg@10 | 0.7964 | |
|
| cosine_mrr@10 | 0.7613 | |
|
| **cosine_map@100** | **0.7649** | |
|
|
|
#### 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 | 0.68 | |
|
| cosine_accuracy@3 | 0.8157 | |
|
| cosine_accuracy@5 | 0.8543 | |
|
| cosine_accuracy@10 | 0.9 | |
|
| cosine_precision@1 | 0.68 | |
|
| cosine_precision@3 | 0.2719 | |
|
| cosine_precision@5 | 0.1709 | |
|
| cosine_precision@10 | 0.09 | |
|
| cosine_recall@1 | 0.68 | |
|
| cosine_recall@3 | 0.8157 | |
|
| cosine_recall@5 | 0.8543 | |
|
| cosine_recall@10 | 0.9 | |
|
| cosine_ndcg@10 | 0.7912 | |
|
| cosine_mrr@10 | 0.7562 | |
|
| **cosine_map@100** | **0.7601** | |
|
|
|
#### 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 | 0.68 | |
|
| cosine_accuracy@3 | 0.8114 | |
|
| cosine_accuracy@5 | 0.8486 | |
|
| cosine_accuracy@10 | 0.8971 | |
|
| cosine_precision@1 | 0.68 | |
|
| cosine_precision@3 | 0.2705 | |
|
| cosine_precision@5 | 0.1697 | |
|
| cosine_precision@10 | 0.0897 | |
|
| cosine_recall@1 | 0.68 | |
|
| cosine_recall@3 | 0.8114 | |
|
| cosine_recall@5 | 0.8486 | |
|
| cosine_recall@10 | 0.8971 | |
|
| cosine_ndcg@10 | 0.7889 | |
|
| cosine_mrr@10 | 0.7542 | |
|
| **cosine_map@100** | **0.758** | |
|
|
|
#### 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 | 0.6571 | |
|
| cosine_accuracy@3 | 0.79 | |
|
| cosine_accuracy@5 | 0.8286 | |
|
| cosine_accuracy@10 | 0.8857 | |
|
| cosine_precision@1 | 0.6571 | |
|
| cosine_precision@3 | 0.2633 | |
|
| cosine_precision@5 | 0.1657 | |
|
| cosine_precision@10 | 0.0886 | |
|
| cosine_recall@1 | 0.6571 | |
|
| cosine_recall@3 | 0.79 | |
|
| cosine_recall@5 | 0.8286 | |
|
| cosine_recall@10 | 0.8857 | |
|
| cosine_ndcg@10 | 0.7704 | |
|
| cosine_mrr@10 | 0.7336 | |
|
| **cosine_map@100** | **0.7379** | |
|
|
|
#### 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 | 0.62 | |
|
| cosine_accuracy@3 | 0.77 | |
|
| cosine_accuracy@5 | 0.8029 | |
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| cosine_accuracy@10 | 0.85 | |
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| cosine_precision@1 | 0.62 | |
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| cosine_precision@3 | 0.2567 | |
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| cosine_precision@5 | 0.1606 | |
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| cosine_precision@10 | 0.085 | |
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| cosine_recall@1 | 0.62 | |
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| cosine_recall@3 | 0.77 | |
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| cosine_recall@5 | 0.8029 | |
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| cosine_recall@10 | 0.85 | |
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| cosine_ndcg@10 | 0.7378 | |
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| cosine_mrr@10 | 0.7016 | |
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| **cosine_map@100** | **0.7074** | |
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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: 46.27 tokens</li><li>max: 326 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.87 tokens</li><li>max: 51 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>We utilize a full yield curve approach in the estimation of service and interest costs by applying the specific spot rates along the yield curve used in the determination of the benefit obligation to the relevant projected cash flows. This approach provides a more precise measurement of service and interest costs by improving the correlation between the projected cash flows to the corresponding spot rates along the yield curve. This approach does not affect the measurement of our pension and other post-retirement benefit liabilities but generally results in lower benefit expense in periods when the yield curve is upward sloping.</code> | <code>How does the use of a full yield curve approach in estimating pension costs affect the measurement of liabilities and expenses?</code> | |
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| <code>Ending | 8,134 | | 8,206 | | 16,340 | | 8,061 | | 8,016 | 16,077</code> | <code>What was the ending store count for the Family Dollar segment after the fiscal year ended January 28, 2023?</code> | |
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| <code>The company's capital expenditures for 2024 are expected to be approximately $5.7 billion.</code> | <code>How much does the company expect to spend on capital expenditures in 2024?</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 |
|
- `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 |
|
|
|
</details> |
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|
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### Training Logs |
|
| 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 | 1.5661 | - | - | - | - | - | |
|
| 0.9746 | 12 | - | 0.7151 | 0.7378 | 0.7443 | 0.6680 | 0.7546 | |
|
| 1.6244 | 20 | 0.6602 | - | - | - | - | - | |
|
| 1.9492 | 24 | - | 0.7326 | 0.7533 | 0.7564 | 0.7037 | 0.7640 | |
|
| 2.4365 | 30 | 0.4675 | - | - | - | - | - | |
|
| 2.9239 | 36 | - | 0.7384 | 0.7575 | 0.7601 | 0.7086 | 0.7643 | |
|
| 3.2487 | 40 | 0.3891 | - | - | - | - | - | |
|
| **3.8985** | **48** | **-** | **0.7379** | **0.758** | **0.7601** | **0.7074** | **0.7649** | |
|
|
|
* The bold row denotes the saved checkpoint. |
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|
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### Framework Versions |
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- Python: 3.10.12 |
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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: 0.32.1 |
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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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|
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### BibTeX |
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|
|
#### Sentence Transformers |
|
```bibtex |
|
@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 |
|
@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}, |
|
eprint={2205.13147}, |
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archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
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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}, |
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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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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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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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