|
--- |
|
language: |
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- en |
|
license: apache-2.0 |
|
tags: |
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- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:6300 |
|
- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
|
base_model: nomic-ai/nomic-embed-text-v1.5 |
|
widget: |
|
- source_sentence: >- |
|
Where in the Annual Report can one find a description of certain legal |
|
matters and their impact on the company? |
|
sentences: |
|
- >- |
|
Apollo coordinates the delivery of new features, security updates, and |
|
platform configurations, ensuring the continuous operation of systems in any |
|
environment. It was introduced commercially in 2021. |
|
- >- |
|
In the Annual Report on Form 10-K, 'Item 1A. Risk Factors' provides a |
|
further description of certain legal matters and their impact on the |
|
company. |
|
- During fiscal 2022, we opened four new stores in Mexico. |
|
- source_sentence: How does the company assess uncertain tax positions? |
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sentences: |
|
- >- |
|
We recognize tax benefits from uncertain tax positions only if we believe |
|
that it is more likely than not that the tax position will be sustained on |
|
examination by the taxing authorities based on the technical merits of the |
|
position. |
|
- >- |
|
CMS uses a risk-adjustment model which adjusts premiums paid to Medicare |
|
Advantage, or MA, plans according to health status of covered members. The |
|
risk-adjustment model, which CMS implemented pursuant to the Balanced Budget |
|
Act of 1997 (BBA) and the Benefits Improvement and Protection Act of 2000 |
|
(BIPA), generally pays more where a plan's membership has higher expected |
|
costs. Under this model, rates paid to MA plans are based on actuarially |
|
determined bids, which include a process whereby our prospective payments |
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are based on our estimated cost of providing standard Medicare-covered |
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benefits to an enrollee with a 'national average risk profile.' That |
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baseline payment amount is adjusted to account for certain demographic |
|
characteristics and health status of our enrolled members. |
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- >- |
|
Walmart Inc. reported total revenues of $611,289 million for the fiscal year |
|
ended January 31, 2023. |
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- source_sentence: >- |
|
When does the 364-day facility entered into in August 2023 expire, and what |
|
is its total amount? |
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sentences: |
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- In 2023, the total revenue generated by Emgality amounted to 678.3. |
|
- >- |
|
In August 2023, we entered into a new 364-day facility. The 364-day facility |
|
of $3.15 billion expires in August 2024. |
|
- >- |
|
Diluted EPS increased $0.09, or 2%, to $5.90 as the decrease in net earnings |
|
was more than fully offset by a reduction in shares outstanding. |
|
- source_sentence: >- |
|
What does the company believe adds significant value to its business |
|
regarding intellectual property? |
|
sentences: |
|
- >- |
|
We believe that, to varying degrees, our trademarks, trade names, |
|
copyrights, proprietary processes, trade secrets, trade dress, domain names |
|
and similar intellectual property add significant value to our business |
|
- >- |
|
Railroad operating revenues declined 6.9% in 2023 compared to 2022, |
|
reflecting an overall volume decrease of 5.7% and a decrease in average |
|
revenue per car/unit of 0.6%, primarily attributable to lower fuel surcharge |
|
revenue, partially offset by favorable price and mix. |
|
- >- |
|
Cash provided by operating activities increased from $26.413 billion in 2022 |
|
to $28.501 billion in 2023, an increase of approximately $2.088 billion. |
|
- source_sentence: >- |
|
How are government incentives treated in accounting according to the given |
|
information? |
|
sentences: |
|
- >- |
|
The components of 'Other income (expense), net' for the year ended December |
|
30, 2023, were $197 million; for December 31, 2022, they were $8 million; |
|
and for December 25, 2021, they were $55 million. |
|
- >- |
|
We are entitled to certain advanced manufacturing production credits under |
|
the IRA, and government incentives are not accounted for or classified as an |
|
income tax credit. We account for government incentives as a reduction of |
|
expense, a reduction of the cost of the capital investment or other income |
|
based on the substance of the incentive received. Benefits are generally |
|
recorded when there is reasonable assurance of receipt or, as it relates |
|
with advanced manufacturing production credits, upon the generation of the |
|
credit. |
|
- >- |
|
Basic net income per share is computed by dividing net income attributable |
|
to common stock by the weighted-average number of shares of common stock |
|
outstanding during the period. |
|
pipeline_tag: sentence-similarity |
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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: Nomic Embed Financial Matryoshka |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7185714285714285 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.87 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9014285714285715 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9357142857142857 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7185714285714285 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.29 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.18028571428571427 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09357142857142857 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7185714285714285 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.87 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9014285714285715 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9357142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8337966812161252 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8004784580498868 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8030662019934727 |
|
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: 0.8685714285714285 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9028571428571428 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9342857142857143 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7157142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2895238095238095 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.18057142857142855 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09342857142857142 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7157142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8685714285714285 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9028571428571428 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9342857142857143 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8320816465681472 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7986201814058957 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8013251784905495 |
|
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: 0.86 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8914285714285715 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9271428571428572 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7028571428571428 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2866666666666667 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17828571428571427 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09271428571428571 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7028571428571428 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.86 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8914285714285715 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9271428571428572 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8208030315973883 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7862023809523814 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7893111186082761 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8428571428571429 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8771428571428571 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9271428571428572 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.28095238095238095 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1754285714285714 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09271428571428571 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8428571428571429 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8771428571428571 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9271428571428572 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8174548081454337 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7820821995464855 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7852661387487447 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.69 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.83 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8671428571428571 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9128571428571428 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.69 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.27666666666666667 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1734285714285714 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09128571428571428 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.69 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.83 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8671428571428571 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9128571428571428 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.804303333645382 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.769315192743764 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7729055647510643 |
|
name: Cosine Map@100 |
|
datasets: |
|
- philschmid/finanical-rag-embedding-dataset |
|
--- |
|
|
|
# Nomic Embed Financial Matryoshka |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co./nomic-ai/nomic-embed-text-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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co./nomic-ai/nomic-embed-text-v1.5) <!-- at revision d802ae16c9caed4d197895d27c6d529434cd8c6d --> |
|
- **Maximum Sequence Length:** 8192 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel |
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
) |
|
``` |
|
|
|
## 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("shail-2512/nomic-embed-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
|
'How are government incentives treated in accounting according to the given information?', |
|
'We are entitled to certain advanced manufacturing production credits under the IRA, and government incentives are not accounted for or classified as an income tax credit. We account for government incentives as a reduction of expense, a reduction of the cost of the capital investment or other income based on the substance of the incentive received. Benefits are generally recorded when there is reasonable assurance of receipt or, as it relates with advanced manufacturing production credits, upon the generation of the credit.', |
|
'Basic net income per share is computed by dividing net income attributable to common stock by the weighted-average number of shares of common stock outstanding during the period.', |
|
] |
|
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 | 0.7186 | 0.7157 | 0.7029 | 0.7 | 0.69 | |
|
| cosine_accuracy@3 | 0.87 | 0.8686 | 0.86 | 0.8429 | 0.83 | |
|
| cosine_accuracy@5 | 0.9014 | 0.9029 | 0.8914 | 0.8771 | 0.8671 | |
|
| cosine_accuracy@10 | 0.9357 | 0.9343 | 0.9271 | 0.9271 | 0.9129 | |
|
| cosine_precision@1 | 0.7186 | 0.7157 | 0.7029 | 0.7 | 0.69 | |
|
| cosine_precision@3 | 0.29 | 0.2895 | 0.2867 | 0.281 | 0.2767 | |
|
| cosine_precision@5 | 0.1803 | 0.1806 | 0.1783 | 0.1754 | 0.1734 | |
|
| cosine_precision@10 | 0.0936 | 0.0934 | 0.0927 | 0.0927 | 0.0913 | |
|
| cosine_recall@1 | 0.7186 | 0.7157 | 0.7029 | 0.7 | 0.69 | |
|
| cosine_recall@3 | 0.87 | 0.8686 | 0.86 | 0.8429 | 0.83 | |
|
| cosine_recall@5 | 0.9014 | 0.9029 | 0.8914 | 0.8771 | 0.8671 | |
|
| cosine_recall@10 | 0.9357 | 0.9343 | 0.9271 | 0.9271 | 0.9129 | |
|
| **cosine_ndcg@10** | **0.8338** | **0.8321** | **0.8208** | **0.8175** | **0.8043** | |
|
| cosine_mrr@10 | 0.8005 | 0.7986 | 0.7862 | 0.7821 | 0.7693 | |
|
| cosine_map@100 | 0.8031 | 0.8013 | 0.7893 | 0.7853 | 0.7729 | |
|
|
|
<!-- |
|
## 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.* |
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--> |
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|
|
<!-- |
|
### 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>anchor</code> and <code>positive</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | anchor | positive | |
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 2 tokens</li><li>mean: 20.65 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 46.29 tokens</li><li>max: 326 tokens</li></ul> | |
|
* Samples: |
|
| anchor | positive | |
|
|:-------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Where is the Investor Relations office of Intuit Inc. located?</code> | <code>Copies of this Annual Report on Form 10-K may also be obtained without charge by contacting Investor Relations, Intuit Inc., P.O. Box 7850, Mountain View, California 94039-7850, calling 650-944-6000, or emailing investor_[email protected].</code> | |
|
| <code>Where is the Financial Statement Schedule located in the Form 10-K?</code> | <code>The Financial Statement Schedule is found on page S-1 of the Form 10-K.</code> | |
|
| <code>What factors are considered when evaluating the realization of deferred tax assets?</code> | <code>Many factors are considered when assessing whether it is more likely than not that the deferred tax assets will be realized, including recent cumulative earnings, expectations of future taxable income, carryforward periods and other relevant quantitative and qualitative factors.</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 |
|
} |
|
``` |
|
|
|
### Evaluation Dataset |
|
|
|
#### json |
|
|
|
* Dataset: json |
|
* Size: 700 evaluation samples |
|
* Columns: <code>anchor</code> and <code>positive</code> |
|
* Approximate statistics based on the first 700 samples: |
|
| | anchor | positive | |
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 2 tokens</li><li>mean: 20.71 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 46.74 tokens</li><li>max: 248 tokens</li></ul> | |
|
* Samples: |
|
| anchor | positive | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>What fiscal changes did Garmin make in January 2023?</code> | <code>The Company announced an organization realignment in January 2023, which combined the consumer auto operating segment with the outdoor operating segment.</code> | |
|
| <code>Where are the details about 'Legal Matters' and 'Government Investigations, Audits and Reviews' located in the financial statements?</code> | <code>The information required by this Item 3 is incorporated herein by reference to the information set forth under the captions 'Legal Matters' and 'Government Investigations, Audits and Reviews' in Note 12 of the Notes to the Consolidated Financial Statements included in Part II, Item 8, 'Financial Statements and Supplementary Data'.</code> | |
|
| <code>Are the pages of IBM's Management’s Discussion and Analysis section in the 2023 Annual Report included in the report itself?</code> | <code>In IBM’s 2023 Annual Report, the pages containing Management’s Discussion and Analysis of Financial Condition and Results of Operations (pages 6 through 40) are incorporated by reference.</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 |
|
- `gradient_accumulation_steps`: 8 |
|
- `learning_rate`: 2e-05 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `bf16`: 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`: 8 |
|
- `per_device_eval_batch_size`: 8 |
|
- `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`: 3 |
|
- `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`: 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`: 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 | Validation 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 | 0.2626 | - | - | - | - | - | - | |
|
| 0.2030 | 20 | 0.1764 | - | - | - | - | - | - | |
|
| 0.1015 | 10 | 0.0311 | - | - | - | - | - | - | |
|
| 0.2030 | 20 | 0.0259 | - | - | - | - | - | - | |
|
| 0.1015 | 10 | 0.0056 | - | - | - | - | - | - | |
|
| 0.2030 | 20 | 0.0064 | - | - | - | - | - | - | |
|
| 0.1015 | 10 | 0.0016 | - | - | - | - | - | - | |
|
| 0.2030 | 20 | 0.0015 | - | - | - | - | - | - | |
|
| 0.1015 | 10 | 0.0006 | - | - | - | - | - | - | |
|
| 0.2030 | 20 | 0.0006 | - | - | - | - | - | - | |
|
| 0.3046 | 30 | 0.1324 | - | - | - | - | - | - | |
|
| 0.4061 | 40 | 0.113 | - | - | - | - | - | - | |
|
| 0.5076 | 50 | 0.128 | - | - | - | - | - | - | |
|
| 0.6091 | 60 | 0.1134 | - | - | - | - | - | - | |
|
| 0.7107 | 70 | 0.056 | - | - | - | - | - | - | |
|
| 0.8122 | 80 | 0.1086 | - | - | - | - | - | - | |
|
| 0.9137 | 90 | 0.1008 | - | - | - | - | - | - | |
|
| **1.0** | **99** | **-** | **0.0771** | **0.8286** | **0.8306** | **0.8266** | **0.8197** | **0.7955** | |
|
| 1.0102 | 100 | 0.0491 | - | - | - | - | - | - | |
|
| 1.1117 | 110 | 0.0029 | - | - | - | - | - | - | |
|
| 1.2132 | 120 | 0.0009 | - | - | - | - | - | - | |
|
| 1.3147 | 130 | 0.0326 | - | - | - | - | - | - | |
|
| 1.4162 | 140 | 0.0077 | - | - | - | - | - | - | |
|
| 1.5178 | 150 | 0.0109 | - | - | - | - | - | - | |
|
| 1.6193 | 160 | 0.0047 | - | - | - | - | - | - | |
|
| 1.7208 | 170 | 0.004 | - | - | - | - | - | - | |
|
| 1.8223 | 180 | 0.0122 | - | - | - | - | - | - | |
|
| 1.9239 | 190 | 0.0043 | - | - | - | - | - | - | |
|
| 2.0 | 198 | - | 0.0758 | 0.8296 | 0.8330 | 0.8222 | 0.8169 | 0.7998 | |
|
| 2.0203 | 200 | 0.0032 | - | - | - | - | - | - | |
|
| 2.1218 | 210 | 0.0002 | - | - | - | - | - | - | |
|
| 2.2234 | 220 | 0.0002 | - | - | - | - | - | - | |
|
| 2.3249 | 230 | 0.0097 | - | - | - | - | - | - | |
|
| 2.4264 | 240 | 0.0012 | - | - | - | - | - | - | |
|
| 2.5279 | 250 | 0.0012 | - | - | - | - | - | - | |
|
| 2.6294 | 260 | 0.0009 | - | - | - | - | - | - | |
|
| 2.7310 | 270 | 0.0007 | - | - | - | - | - | - | |
|
| 2.8325 | 280 | 0.0019 | - | - | - | - | - | - | |
|
| 2.9340 | 290 | 0.0009 | - | - | - | - | - | - | |
|
| 2.9746 | 294 | - | 0.0744 | 0.8338 | 0.8321 | 0.8208 | 0.8175 | 0.8043 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.47.0 |
|
- PyTorch: 2.5.1+cu121 |
|
- Accelerate: 1.1.1 |
|
- Datasets: 3.1.0 |
|
- 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} |
|
} |
|
``` |
|
|
|
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