|
--- |
|
language: |
|
- en |
|
license: apache-2.0 |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:6300 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
base_model: BAAI/bge-base-en-v1.5 |
|
widget: |
|
- source_sentence: Health Care Benefits revenue is principally derived from insurance |
|
premiums and fees billed to customers. |
|
sentences: |
|
- How much was the cumulative impairment and downward adjustments for observable |
|
price changes for the equity investments without readily determinable fair values |
|
as of December 31, 2023? |
|
- What are the revenue sources for the Company’s Health Care Benefits Segment? |
|
- What types of legal issues are generally categorized under Commitments and Contingencies |
|
in a Form 10-K? |
|
- source_sentence: Total net sales increased by 7% during the fiscal year ending December |
|
30, 2023 compared to the previous fiscal year. |
|
sentences: |
|
- What was the percentage increase in Data Center revenue for fiscal year 2023 compared |
|
to the previous year? |
|
- What was the percentage increase in total net sales during the fiscal year ending |
|
December 30, 2023 compared to the previous fiscal year? |
|
- What were the expenses related to the fair value of restricted stock units (RSUs) |
|
and stock options for the years 2022, 2021, and 2020? |
|
- source_sentence: The laws and regulations of the jurisdictions in which our insurance |
|
and reinsurance subsidiaries are domiciled require among other things that these |
|
subsidiaries maintain minimum levels of statutory capital, surplus, and liquidity, |
|
meet solvency standards, and submit to periodic examinations of their financial |
|
condition. |
|
sentences: |
|
- What statutory requirements must insurance and reinsurance subsidiaries meet in |
|
their domiciled jurisdictions? |
|
- What activities has the federal government used the FCA to prosecute? |
|
- How are self-insurance reserves computed and presented in financial statements? |
|
- source_sentence: Services net sales increased 9% or $7.1 billion during 2023 compared |
|
to 2022 due to higher net sales across all lines of business. |
|
sentences: |
|
- What is the leverage ratio requirement under the company's financial covenant |
|
as of January 28, 2023? |
|
- What are the enrollment periods for Medicare Advantage and stand-alone prescription |
|
drug plans? |
|
- What was the percentage increase in Services net sales from 2022 to 2023? |
|
- source_sentence: Certain vendors have been impacted by volatility in the supply |
|
chain financing market. |
|
sentences: |
|
- How have certain vendors been impacted in the supply chain financing market? |
|
- What was the total value of the company's cash commitments as of December 31, |
|
2023? |
|
- What are the key components used to define free cash flow in financial evaluations? |
|
pipeline_tag: sentence-similarity |
|
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 |
|
model-index: |
|
- name: BGE base Financial Matryoshka |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6871428571428572 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8171428571428572 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8485714285714285 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9085714285714286 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6871428571428572 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2723809523809524 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16971428571428568 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09085714285714284 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6871428571428572 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8171428571428572 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8485714285714285 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9085714285714286 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7960378752604689 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7603769841269836 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7640840138316877 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6828571428571428 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8114285714285714 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8528571428571429 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9085714285714286 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6828571428571428 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2704761904761904 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17057142857142857 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09085714285714284 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6828571428571428 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8114285714285714 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8528571428571429 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9085714285714286 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7936620196836198 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7572222222222219 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7609298999926937 |
|
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: 0.8071428571428572 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8485714285714285 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8957142857142857 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.68 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26904761904761904 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16971428571428568 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08957142857142855 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.68 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8071428571428572 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8485714285714285 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8957142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7883110340362532 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7539733560090701 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7582685695127231 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6585714285714286 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7942857142857143 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.83 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8842857142857142 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6585714285714286 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26476190476190475 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16599999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08842857142857141 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6585714285714286 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7942857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.83 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8842857142857142 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7727884715594033 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.737036848072562 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7419081242961935 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6357142857142857 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7628571428571429 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8142857142857143 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.87 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6357142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2542857142857142 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16285714285714287 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.087 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6357142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7628571428571429 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8142857142857143 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.87 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7501277228250628 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7121167800453513 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7171110018302509 |
|
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) on the json dataset. 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 dimensions |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
|
- json |
|
- **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("MarekMarik/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
|
'Certain vendors have been impacted by volatility in the supply chain financing market.', |
|
'How have certain vendors been impacted in the supply chain financing market?', |
|
"What was the total value of the company's cash commitments as of December 31, 2023?", |
|
] |
|
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 |
|
|
|
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |
|
|:--------------------|:----------|:-----------|:-----------|:-----------|:-----------| |
|
| cosine_accuracy@1 | 0.6871 | 0.6829 | 0.68 | 0.6586 | 0.6357 | |
|
| cosine_accuracy@3 | 0.8171 | 0.8114 | 0.8071 | 0.7943 | 0.7629 | |
|
| cosine_accuracy@5 | 0.8486 | 0.8529 | 0.8486 | 0.83 | 0.8143 | |
|
| cosine_accuracy@10 | 0.9086 | 0.9086 | 0.8957 | 0.8843 | 0.87 | |
|
| cosine_precision@1 | 0.6871 | 0.6829 | 0.68 | 0.6586 | 0.6357 | |
|
| cosine_precision@3 | 0.2724 | 0.2705 | 0.269 | 0.2648 | 0.2543 | |
|
| cosine_precision@5 | 0.1697 | 0.1706 | 0.1697 | 0.166 | 0.1629 | |
|
| cosine_precision@10 | 0.0909 | 0.0909 | 0.0896 | 0.0884 | 0.087 | |
|
| cosine_recall@1 | 0.6871 | 0.6829 | 0.68 | 0.6586 | 0.6357 | |
|
| cosine_recall@3 | 0.8171 | 0.8114 | 0.8071 | 0.7943 | 0.7629 | |
|
| cosine_recall@5 | 0.8486 | 0.8529 | 0.8486 | 0.83 | 0.8143 | |
|
| cosine_recall@10 | 0.9086 | 0.9086 | 0.8957 | 0.8843 | 0.87 | |
|
| **cosine_ndcg@10** | **0.796** | **0.7937** | **0.7883** | **0.7728** | **0.7501** | |
|
| cosine_mrr@10 | 0.7604 | 0.7572 | 0.754 | 0.737 | 0.7121 | |
|
| cosine_map@100 | 0.7641 | 0.7609 | 0.7583 | 0.7419 | 0.7171 | |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### json |
|
|
|
* Dataset: json |
|
* Size: 6,300 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 8 tokens</li><li>mean: 45.84 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.62 tokens</li><li>max: 42 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>We adopted SAB 121 during fiscal 2022, with no impact on our consolidated financial statements.</code> | <code>What accounting guidance did the company adopt in fiscal 2022 and what was its impact on the consolidated financial statements?</code> | |
|
| <code>Mortgage Solutions revenue decreased 18% in 2023 compared to 2022, due to significantly lower mortgage credit inquiry volumes in 2023 compared to the prior year.</code> | <code>What caused the 18% decline in Mortgage Solutions revenue in 2023 compared to 2022?</code> | |
|
| <code>Adoption of SBTi goals would build on our current science-based goals to reduce Scope 1 and 2 carbon emissions by 2.1% per year, to achieve a 40% reduction by the end of fiscal 2030 and a 50% reduction by the end of fiscal 2035.</code> | <code>What is the company's percentage target for reducing Scope 1 and 2 carbon emissions by end of fiscal 2035?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_eval_batch_size`: 4 |
|
- `gradient_accumulation_steps`: 8 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 4 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `bf16`: True |
|
- `tf32`: False |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 8 |
|
- `per_device_eval_batch_size`: 4 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 8 |
|
- `eval_accumulation_steps`: None |
|
- `torch_empty_cache_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 4 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: False |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: True |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: None |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `include_for_metrics`: [] |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
|
- `prompts`: None |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |
|
|:-------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.1015 | 10 | 0.614 | - | - | - | - | - | |
|
| 0.2030 | 20 | 0.5098 | - | - | - | - | - | |
|
| 0.3046 | 30 | 0.426 | - | - | - | - | - | |
|
| 0.4061 | 40 | 0.3262 | - | - | - | - | - | |
|
| 0.5076 | 50 | 0.2131 | - | - | - | - | - | |
|
| 0.6091 | 60 | 0.1892 | - | - | - | - | - | |
|
| 0.7107 | 70 | 0.3049 | - | - | - | - | - | |
|
| 0.8122 | 80 | 0.1617 | - | - | - | - | - | |
|
| 0.9137 | 90 | 0.1214 | - | - | - | - | - | |
|
| 1.0 | 99 | - | 0.7895 | 0.7919 | 0.7800 | 0.7685 | 0.7361 | |
|
| 1.0102 | 100 | 0.147 | - | - | - | - | - | |
|
| 1.1117 | 110 | 0.0938 | - | - | - | - | - | |
|
| 1.2132 | 120 | 0.1406 | - | - | - | - | - | |
|
| 1.3147 | 130 | 0.1058 | - | - | - | - | - | |
|
| 1.4162 | 140 | 0.1072 | - | - | - | - | - | |
|
| 1.5178 | 150 | 0.0352 | - | - | - | - | - | |
|
| 1.6193 | 160 | 0.0568 | - | - | - | - | - | |
|
| 1.7208 | 170 | 0.1283 | - | - | - | - | - | |
|
| 1.8223 | 180 | 0.066 | - | - | - | - | - | |
|
| 1.9239 | 190 | 0.038 | - | - | - | - | - | |
|
| 2.0 | 198 | - | 0.7945 | 0.7945 | 0.7860 | 0.7736 | 0.7462 | |
|
| 2.0203 | 200 | 0.0544 | - | - | - | - | - | |
|
| 2.1218 | 210 | 0.0333 | - | - | - | - | - | |
|
| 2.2234 | 220 | 0.042 | - | - | - | - | - | |
|
| 2.3249 | 230 | 0.0489 | - | - | - | - | - | |
|
| 2.4264 | 240 | 0.0498 | - | - | - | - | - | |
|
| 2.5279 | 250 | 0.0119 | - | - | - | - | - | |
|
| 2.6294 | 260 | 0.0273 | - | - | - | - | - | |
|
| 2.7310 | 270 | 0.0719 | - | - | - | - | - | |
|
| 2.8325 | 280 | 0.0366 | - | - | - | - | - | |
|
| 2.9340 | 290 | 0.0333 | - | - | - | - | - | |
|
| **3.0** | **297** | **-** | **0.7927** | **0.7952** | **0.7881** | **0.7743** | **0.7477** | |
|
| 3.0305 | 300 | 0.0193 | - | - | - | - | - | |
|
| 3.1320 | 310 | 0.0254 | - | - | - | - | - | |
|
| 3.2335 | 320 | 0.0252 | - | - | - | - | - | |
|
| 3.3350 | 330 | 0.039 | - | - | - | - | - | |
|
| 3.4365 | 340 | 0.0224 | - | - | - | - | - | |
|
| 3.5381 | 350 | 0.0091 | - | - | - | - | - | |
|
| 3.6396 | 360 | 0.0356 | - | - | - | - | - | |
|
| 3.7411 | 370 | 0.042 | - | - | - | - | - | |
|
| 3.8426 | 380 | 0.038 | - | - | - | - | - | |
|
| 3.9442 | 390 | 0.0088 | - | - | - | - | - | |
|
| 3.9645 | 392 | - | 0.7960 | 0.7937 | 0.7883 | 0.7728 | 0.7501 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.12.8 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.47.1 |
|
- PyTorch: 2.5.1+cu124 |
|
- Accelerate: 1.2.1 |
|
- Datasets: 3.2.0 |
|
- Tokenizers: 0.21.0 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
