|
--- |
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base_model: BAAI/bge-base-en-v1.5 |
|
datasets: [] |
|
language: |
|
- en |
|
library_name: sentence-transformers |
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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_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 December 31, 2023, deferred revenues for unsatisfied performance |
|
obligations consisted of $769 million related to Hilton Honors that will be recognized |
|
as revenue over approximately the next two years. |
|
sentences: |
|
- How many shares of common stock were issued in both 2022 and 2023? |
|
- What is the projected timeline for recognizing revenue from deferred revenues |
|
related to Hilton Honors as of December 31, 2023? |
|
- What acquisitions did CVS Health Corporation complete in 2023 to enhance their |
|
care delivery strategy? |
|
- source_sentence: If a good or service does not qualify as distinct, it is combined |
|
with the other non-distinct goods or services within the arrangement and these |
|
combined goods or services are treated as a single performance obligation for |
|
accounting purposes. The arrangement's transaction price is then allocated to |
|
each performance obligation based on the relative standalone selling price of |
|
each performance obligation. |
|
sentences: |
|
- What does the summary table indicate about the company's activities at the end |
|
of 2023? |
|
- What governs the treatment of goods or services that are not distinct within a |
|
contractual arrangement? |
|
- What is the basis for the Company to determine the Standalone Selling Price (SSP) |
|
for each distinct performance obligation in contracts with multiple performance |
|
obligations? |
|
- source_sentence: As of January 2023, the maximum daily borrowing capacity under |
|
the commercial paper program was approximately $2.75 billion. |
|
sentences: |
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- What is the maximum daily borrowing capacity under the commercial paper program |
|
as of January 2023? |
|
- When does the Company's fiscal year end? |
|
- How much cash did acquisition activities use in 2023? |
|
- source_sentence: Federal Home Loan Bank borrowings had an interest rate of 4.59% |
|
in 2022, which increased to 5.14% in 2023. |
|
sentences: |
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- By what percentage did the company's capital expenditures increase in fiscal 2023 |
|
compared to fiscal 2022? |
|
- What is the significance of Note 13 in the context of legal proceedings described |
|
in the Annual Report on Form 10-K? |
|
- How much did the Federal Home Loan Bank borrowings increase in terms of interest |
|
rates from 2022 to 2023? |
|
- source_sentence: The design of the Annual Report, with the consolidated financial |
|
statements placed immediately after Part IV, enhances the integration of financial |
|
data by maintaining a coherent structure. |
|
sentences: |
|
- How does the structure of the Annual Report on Form 10-K facilitate the integration |
|
of the consolidated financial statements? |
|
- Where can one find the Glossary of Terms and Acronyms in Item 8? |
|
- What part of the annual report contains the consolidated financial statements |
|
and accompanying notes? |
|
model-index: |
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- name: BGE base Financial Matryoshka |
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results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6957142857142857 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8171428571428572 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8628571428571429 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6957142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2723809523809524 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17257142857142854 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08999999999999998 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6957142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8171428571428572 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8628571428571429 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7971144469297426 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7641831065759639 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7681728985040082 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6942857142857143 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.81 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8514285714285714 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6942857142857143 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.27 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17028571428571426 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6942857142857143 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.81 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8514285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7951260604161544 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7617998866213151 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7658003405075238 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7014285714285714 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7971428571428572 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.85 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8885714285714286 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7014285714285714 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26571428571428574 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16999999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08885714285714284 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7014285714285714 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7971428571428572 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.85 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8885714285714286 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.793266992460996 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7629580498866213 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7678096436855835 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6957142857142857 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8014285714285714 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8357142857142857 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8842857142857142 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6957142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2671428571428571 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16714285714285712 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08842857142857141 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6957142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8014285714285714 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8357142857142857 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8842857142857142 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.787378246207931 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7566984126984126 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7613545312565108 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6571428571428571 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7871428571428571 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8285714285714286 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8757142857142857 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6571428571428571 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2623809523809524 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1657142857142857 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08757142857142856 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6571428571428571 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7871428571428571 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8285714285714286 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8757142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7655516319615892 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7303951247165531 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7349875161463472 |
|
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("rbhatia46/bge-base-financial-nvidia-matryoshka") |
|
# Run inference |
|
sentences = [ |
|
'The design of the Annual Report, with the consolidated financial statements placed immediately after Part IV, enhances the integration of financial data by maintaining a coherent structure.', |
|
'How does the structure of the Annual Report on Form 10-K facilitate the integration of the consolidated financial statements?', |
|
'Where can one find the Glossary of Terms and Acronyms in Item 8?', |
|
] |
|
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 | 0.6957 | |
|
| cosine_accuracy@3 | 0.8171 | |
|
| cosine_accuracy@5 | 0.8629 | |
|
| cosine_accuracy@10 | 0.9 | |
|
| cosine_precision@1 | 0.6957 | |
|
| cosine_precision@3 | 0.2724 | |
|
| cosine_precision@5 | 0.1726 | |
|
| cosine_precision@10 | 0.09 | |
|
| cosine_recall@1 | 0.6957 | |
|
| cosine_recall@3 | 0.8171 | |
|
| cosine_recall@5 | 0.8629 | |
|
| cosine_recall@10 | 0.9 | |
|
| cosine_ndcg@10 | 0.7971 | |
|
| cosine_mrr@10 | 0.7642 | |
|
| **cosine_map@100** | **0.7682** | |
|
|
|
#### 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 | 0.6943 | |
|
| cosine_accuracy@3 | 0.81 | |
|
| cosine_accuracy@5 | 0.8514 | |
|
| cosine_accuracy@10 | 0.9 | |
|
| cosine_precision@1 | 0.6943 | |
|
| cosine_precision@3 | 0.27 | |
|
| cosine_precision@5 | 0.1703 | |
|
| cosine_precision@10 | 0.09 | |
|
| cosine_recall@1 | 0.6943 | |
|
| cosine_recall@3 | 0.81 | |
|
| cosine_recall@5 | 0.8514 | |
|
| cosine_recall@10 | 0.9 | |
|
| cosine_ndcg@10 | 0.7951 | |
|
| cosine_mrr@10 | 0.7618 | |
|
| **cosine_map@100** | **0.7658** | |
|
|
|
#### 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 | 0.7014 | |
|
| cosine_accuracy@3 | 0.7971 | |
|
| cosine_accuracy@5 | 0.85 | |
|
| cosine_accuracy@10 | 0.8886 | |
|
| cosine_precision@1 | 0.7014 | |
|
| cosine_precision@3 | 0.2657 | |
|
| cosine_precision@5 | 0.17 | |
|
| cosine_precision@10 | 0.0889 | |
|
| cosine_recall@1 | 0.7014 | |
|
| cosine_recall@3 | 0.7971 | |
|
| cosine_recall@5 | 0.85 | |
|
| cosine_recall@10 | 0.8886 | |
|
| cosine_ndcg@10 | 0.7933 | |
|
| cosine_mrr@10 | 0.763 | |
|
| **cosine_map@100** | **0.7678** | |
|
|
|
#### 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 | 0.6957 | |
|
| cosine_accuracy@3 | 0.8014 | |
|
| cosine_accuracy@5 | 0.8357 | |
|
| cosine_accuracy@10 | 0.8843 | |
|
| cosine_precision@1 | 0.6957 | |
|
| cosine_precision@3 | 0.2671 | |
|
| cosine_precision@5 | 0.1671 | |
|
| cosine_precision@10 | 0.0884 | |
|
| cosine_recall@1 | 0.6957 | |
|
| cosine_recall@3 | 0.8014 | |
|
| cosine_recall@5 | 0.8357 | |
|
| cosine_recall@10 | 0.8843 | |
|
| cosine_ndcg@10 | 0.7874 | |
|
| cosine_mrr@10 | 0.7567 | |
|
| **cosine_map@100** | **0.7614** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | Value | |
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|:--------------------|:----------| |
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| cosine_accuracy@1 | 0.6571 | |
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| cosine_accuracy@3 | 0.7871 | |
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| cosine_accuracy@5 | 0.8286 | |
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| cosine_accuracy@10 | 0.8757 | |
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| cosine_precision@1 | 0.6571 | |
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| cosine_precision@3 | 0.2624 | |
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| cosine_precision@5 | 0.1657 | |
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| cosine_precision@10 | 0.0876 | |
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| cosine_recall@1 | 0.6571 | |
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| cosine_recall@3 | 0.7871 | |
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| cosine_recall@5 | 0.8286 | |
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| cosine_recall@10 | 0.8757 | |
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| cosine_ndcg@10 | 0.7656 | |
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| cosine_mrr@10 | 0.7304 | |
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| **cosine_map@100** | **0.735** | |
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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: |
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| | positive | anchor | |
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|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 45.53 tokens</li><li>max: 222 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.3 tokens</li><li>max: 45 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
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|:---------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------| |
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| <code>Acquisition activity used cash of $765 million in 2023, primarily related to a Beauty acquisition.</code> | <code>How much cash did acquisition activities use in 2023?</code> | |
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| <code>In a financial report, Part IV Item 15 includes Exhibits and Financial Statement Schedules as mentioned.</code> | <code>What content can be expected under Part IV Item 15 in a financial report?</code> | |
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| <code>we had more than 8.3 million fiber consumer wireline broadband customers, adding 1.1 million during the year.</code> | <code>How many fiber consumer wireline broadband customers did the company have at the end of the year?</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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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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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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|
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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 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
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| 0.8122 | 10 | 1.5751 | - | - | - | - | - | |
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| 0.9746 | 12 | - | - | - | - | - | 0.7580 | |
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| 0.8122 | 10 | 0.6362 | - | - | - | - | - | |
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| 0.9746 | 12 | - | 0.7503 | 0.7576 | 0.7653 | 0.7282 | 0.7638 | |
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| 1.6244 | 20 | 0.4426 | - | - | - | - | - | |
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| 1.9492 | 24 | - | 0.7544 | 0.7662 | 0.7640 | 0.7311 | 0.7676 | |
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| 2.4365 | 30 | 0.3217 | - | - | - | - | - | |
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| 2.9239 | 36 | - | 0.7608 | 0.7684 | 0.7662 | 0.7341 | 0.7686 | |
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| 3.2487 | 40 | 0.2761 | - | - | - | - | - | |
|
| **3.8985** | **48** | **-** | **0.7614** | **0.7678** | **0.7658** | **0.735** | **0.7682** | |
|
|
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* The bold row denotes the saved checkpoint. |
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|
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### Framework Versions |
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- Python: 3.10.6 |
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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: 0.31.0 |
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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 |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MatryoshkaLoss |
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```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}, |
|
eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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|
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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}, |
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year={2017}, |
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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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*Clearly define terms in order to be accessible across audiences.* |
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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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