|
--- |
|
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: We enter into forward currency contracts in order to hedge a portion |
|
of the foreign currency exposure associated with the translation of our net investment |
|
in our Canadian subsidiary. |
|
sentences: |
|
- How much did Delta Air Lines spend on debt and finance lease obligations in 2023? |
|
- What mechanisms does the company use to hedge foreign currency exposure for its |
|
Canadian subsidiary? |
|
- How did operating overhead expenses change for NIKE from fiscal 2022 to fiscal |
|
2023? |
|
- source_sentence: We calculate return on invested hat capital (ROIC) by dividing |
|
adjusted ROIC operating profit for the prior four quarters by the average invested |
|
capital. |
|
sentences: |
|
- What was the fair value of U.S. government and agency securities as of June 30, |
|
2022? |
|
- How is the Return on Invested Capital (ROIC) calculated? |
|
- What business outcomes is HPE focused on accelerating with its technological solutions? |
|
- source_sentence: Expenses from our comparable owned and leased hotels increased |
|
$137 million, on a currency neutral basis, as a result of increased occupancy |
|
and cost inflation both driving higher labor costs, utilities and other operating |
|
expenses, as well as an increase in rent expense. |
|
sentences: |
|
- How did the expenses from comparable owned and leased hotels change and what were |
|
the contributing factors? |
|
- What do environmental laws require from suppliers in terms of operations? |
|
- What energy management technologies does the Enphase bidirectional EV charger |
|
integrate with? |
|
- source_sentence: The Advancing Agility & Automation Initiative at The Hershey Company |
|
is projected to result in total pre-tax costs of $200,000 to $250,000 from inception |
|
through 2026. This includes costs for program office execution and third-party |
|
costs supporting the design and implementation of the new organizational structure, |
|
as well as implementation and technology capability costs and employee severance |
|
and related separation benefits. |
|
sentences: |
|
- What was the total amortization expense for The Hershey Company in 2021? |
|
- How much did net cash used in financing activities decrease in fiscal 2023 compared |
|
to the previous fiscal year? |
|
- What is the total projected pre-tax cost of The Hershey Company's Advancing Agility |
|
& Automation Initiative through 2026? |
|
- source_sentence: Structural costs typically do not have a directly proportionate |
|
relationship to production volume and include costs such as manufacturing, engineering, |
|
and administrative expenses. These costs can be adjusted over time in response |
|
to external factors. |
|
sentences: |
|
- How does Ford Motor Company handle its structural costs in relation to production |
|
volume changes? |
|
- What were the total future minimum lease payments under all non-cancelable operating |
|
leases for the company as of December 31, 2023? |
|
- What guidelines does the FASB provide for the measurement of fair value when quoted |
|
prices are not available? |
|
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: 0.72 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8257142857142857 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8585714285714285 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8942857142857142 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.72 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2752380952380953 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1717142857142857 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08942857142857143 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.72 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8257142857142857 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8585714285714285 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8942857142857142 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8077694527772951 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7800079365079364 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7837848752496734 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7157142857142857 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8242857142857143 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8642857142857143 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8914285714285715 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7157142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2747619047619047 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17285714285714285 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08914285714285713 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7157142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8242857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8642857142857143 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8914285714285715 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.805259563189015 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7773735827664396 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7813006780341183 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7028571428571428 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8171428571428572 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8542857142857143 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8814285714285715 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7028571428571428 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2723809523809524 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17085714285714285 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08814285714285712 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7028571428571428 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8171428571428572 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8542857142857143 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8814285714285715 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7945503213768784 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7664075963718817 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7709929668571353 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6785714285714286 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8028571428571428 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8542857142857143 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8814285714285715 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6785714285714286 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26761904761904765 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17085714285714285 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08814285714285712 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6785714285714286 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8028571428571428 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8542857142857143 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8814285714285715 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7829387132685872 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7509529478458048 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7549309056916426 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6485714285714286 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.77 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8142857142857143 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8657142857142858 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6485714285714286 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2566666666666667 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16285714285714287 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08657142857142856 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6485714285714286 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.77 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8142857142857143 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8657142857142858 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.755512484642688 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7203905895691608 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7247515061294347 |
|
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("aired/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
|
'Structural costs typically do not have a directly proportionate relationship to production volume and include costs such as manufacturing, engineering, and administrative expenses. These costs can be adjusted over time in response to external factors.', |
|
'How does Ford Motor Company handle its structural costs in relation to production volume changes?', |
|
'What were the total future minimum lease payments under all non-cancelable operating leases for the company 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 | 0.72 | 0.7157 | 0.7029 | 0.6786 | 0.6486 | |
|
| cosine_accuracy@3 | 0.8257 | 0.8243 | 0.8171 | 0.8029 | 0.77 | |
|
| cosine_accuracy@5 | 0.8586 | 0.8643 | 0.8543 | 0.8543 | 0.8143 | |
|
| cosine_accuracy@10 | 0.8943 | 0.8914 | 0.8814 | 0.8814 | 0.8657 | |
|
| cosine_precision@1 | 0.72 | 0.7157 | 0.7029 | 0.6786 | 0.6486 | |
|
| cosine_precision@3 | 0.2752 | 0.2748 | 0.2724 | 0.2676 | 0.2567 | |
|
| cosine_precision@5 | 0.1717 | 0.1729 | 0.1709 | 0.1709 | 0.1629 | |
|
| cosine_precision@10 | 0.0894 | 0.0891 | 0.0881 | 0.0881 | 0.0866 | |
|
| cosine_recall@1 | 0.72 | 0.7157 | 0.7029 | 0.6786 | 0.6486 | |
|
| cosine_recall@3 | 0.8257 | 0.8243 | 0.8171 | 0.8029 | 0.77 | |
|
| cosine_recall@5 | 0.8586 | 0.8643 | 0.8543 | 0.8543 | 0.8143 | |
|
| cosine_recall@10 | 0.8943 | 0.8914 | 0.8814 | 0.8814 | 0.8657 | |
|
| **cosine_ndcg@10** | **0.8078** | **0.8053** | **0.7946** | **0.7829** | **0.7555** | |
|
| cosine_mrr@10 | 0.78 | 0.7774 | 0.7664 | 0.751 | 0.7204 | |
|
| cosine_map@100 | 0.7838 | 0.7813 | 0.771 | 0.7549 | 0.7248 | |
|
|
|
<!-- |
|
## 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: 9 tokens</li><li>mean: 45.81 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.45 tokens</li><li>max: 42 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------| |
|
| <code>GEICO markets its policies mainly by direct response methods where most customers apply for coverage directly to the company via the Internet or over the telephone.</code> | <code>What are the primary marketing methods used by GEICO?</code> | |
|
| <code>In addition, most group health plans and issuers of group or individual health insurance coverage are required to disclose personalized pricing information to their participants, beneficiaries, and enrollees through an online consumer tool, by phone, or in paper form, upon request. Cost estimates must be provided in real-time based on cost-sharing information that is accurate at the time of the request.</code> | <code>What are the requirements for health insurers and group health plans in providing cost estimates to consumers?</code> | |
|
| <code>Fair values of indefinite-lived intangible assets are determined based on the income approach.</code> | <code>What method is used to determine the fair value of indefinite-lived intangible assets?</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_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `gradient_accumulation_steps`: 16 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 4 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `fp16`: True |
|
- `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`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 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`: False |
|
- `fp16`: True |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: None |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: True |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `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 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.8122 | 10 | 1.6045 | - | - | - | - | - | |
|
| 0.9746 | 12 | - | 0.7895 | 0.7895 | 0.7764 | 0.7680 | 0.7277 | |
|
| 1.6244 | 20 | 0.6975 | - | - | - | - | - | |
|
| 1.9492 | 24 | - | 0.8044 | 0.8026 | 0.7924 | 0.7819 | 0.7515 | |
|
| 2.4365 | 30 | 0.4732 | - | - | - | - | - | |
|
| 2.9239 | 36 | - | 0.8064 | 0.8060 | 0.7944 | 0.7825 | 0.7549 | |
|
| 3.2487 | 40 | 0.4182 | - | - | - | - | - | |
|
| **3.8985** | **48** | **-** | **0.8078** | **0.8053** | **0.7946** | **0.7829** | **0.7555** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 1.1.1 |
|
- Datasets: 2.19.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
<!-- |
|
## 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.* |
|
--> |
