|
--- |
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base_model: BAAI/bge-base-en-v1.5 |
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datasets: [] |
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language: |
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- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
|
- cosine_accuracy@5 |
|
- cosine_accuracy@10 |
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- cosine_precision@1 |
|
- cosine_precision@3 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
|
- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:6300 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: A number of factors may impact ESKD growth rates, including mortality |
|
rates for dialysis patients or CKD patients, the aging of the U.S. population, |
|
transplant rates, incidence rates for diseases that cause kidney failure such |
|
as diabetes and hypertension, growth rates of minority populations with higher |
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than average incidence rates of ESKD. |
|
sentences: |
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- By how much did the company increase its quarterly cash dividend in February 2023? |
|
- What factors may impact the growth rates of the ESKD patient population? |
|
- What percentage increase did salaries and related costs experience at Delta Air |
|
Lines from 2022 to 2023? |
|
- source_sentence: HIV product sales increased 6% to $18.2 billion in 2023, compared |
|
to 2022. |
|
sentences: |
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- What were the present values of lease liabilities for operating and finance leases |
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as of December 31, 2023? |
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- By what percentage did HIV product sales increase in 2023 compared to the previous |
|
year? |
|
- How is interest income not attributable to the Card Member loan portfolio primarily |
|
represented in financial documents? |
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- source_sentence: If a violation is found, a broad range of remedies is potentially |
|
available to the Commission and/or CMA, including imposing a fine and/or the prohibition |
|
or restriction of certain business practices. |
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sentences: |
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- What are the potential remedies if a violation is found by the European Commission |
|
or the U.K. Competition and Markets Authority in their investigation of automotive |
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companies? |
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- By which auditing standards were the consolidated financial statements of Salesforce, |
|
Inc. audited? |
|
- What is the main role of Kroger's Chief Executive Officer in the company? |
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- source_sentence: The discussion in Hewlett Packard Enterprise's Form 10-K highlights |
|
factors impacting costs and revenues, including easing supply chain constraints, |
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foreign exchange pressures, inflationary trends, and recent tax developments potentially |
|
affecting their financial outcomes. |
|
sentences: |
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- Is the outcome of the investigation into Tesla's waste segregation practices currently |
|
determinable? |
|
- How does Hewlett Packard Enterprise justify the exclusion of transformation costs |
|
from its non-GAAP financial measures? |
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- In the context of Hewlett Packard Enterprise's recent financial discussions, what |
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factors are expected to impact their operational costs and revenue growth moving |
|
forward? |
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- source_sentence: Our Records Management and Data Management service revenue growth |
|
is being negatively impacted by declining activity rates as stored records and |
|
tapes are becoming less active and more archival. |
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sentences: |
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- How is Iron Mountain addressing the decline in activity rates in their Records |
|
and Data Management services? |
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- What services do companies that build fiber-based networks provide in the Connectivity |
|
& Platforms markets? |
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- What business outcomes is HPE focused on accelerating with its technological solutions? |
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model-index: |
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- name: BGE base Financial Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
|
name: Information Retrieval |
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dataset: |
|
name: dim 768 |
|
type: dim_768 |
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metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7057142857142857 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8457142857142858 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8785714285714286 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9114285714285715 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7057142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2819047619047619 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17571428571428568 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09114285714285714 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7057142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8457142857142858 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8785714285714286 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9114285714285715 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8125296344519609 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7804263038548749 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7839408125709297 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7071428571428572 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8428571428571429 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8742857142857143 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9114285714285715 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7071428571428572 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.28095238095238095 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17485714285714282 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09114285714285714 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7071428571428572 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8428571428571429 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8742857142857143 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9114285714285715 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8126517351231356 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7807267573696143 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7841188299664252 |
|
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: 0.8357142857142857 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8685714285714285 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9071428571428571 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7028571428571428 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2785714285714286 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1737142857142857 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09071428571428572 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7028571428571428 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8357142857142857 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8685714285714285 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9071428571428571 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8086618947757659 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7768820861678005 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7806177775944575 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6914285714285714 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.82 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8557142857142858 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9014285714285715 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6914285714285714 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2733333333333334 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17114285714285712 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09014285714285714 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6914285714285714 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.82 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8557142857142858 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9014285714285715 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7980982703041672 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7650045351473919 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7688564414027702 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6542857142857142 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7885714285714286 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8328571428571429 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8828571428571429 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6542857142857142 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26285714285714284 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16657142857142856 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08828571428571427 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6542857142857142 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7885714285714286 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8328571428571429 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8828571428571429 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7689665884678363 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7325351473922898 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7369423610264151 |
|
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("NickyNicky/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
|
'Our Records Management and Data Management service revenue growth is being negatively impacted by declining activity rates as stored records and tapes are becoming less active and more archival.', |
|
'How is Iron Mountain addressing the decline in activity rates in their Records and Data Management services?', |
|
'What services do companies that build fiber-based networks provide in the Connectivity & Platforms markets?', |
|
] |
|
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 | 0.7057 | |
|
| cosine_accuracy@3 | 0.8457 | |
|
| cosine_accuracy@5 | 0.8786 | |
|
| cosine_accuracy@10 | 0.9114 | |
|
| cosine_precision@1 | 0.7057 | |
|
| cosine_precision@3 | 0.2819 | |
|
| cosine_precision@5 | 0.1757 | |
|
| cosine_precision@10 | 0.0911 | |
|
| cosine_recall@1 | 0.7057 | |
|
| cosine_recall@3 | 0.8457 | |
|
| cosine_recall@5 | 0.8786 | |
|
| cosine_recall@10 | 0.9114 | |
|
| cosine_ndcg@10 | 0.8125 | |
|
| cosine_mrr@10 | 0.7804 | |
|
| **cosine_map@100** | **0.7839** | |
|
|
|
#### 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 | 0.7071 | |
|
| cosine_accuracy@3 | 0.8429 | |
|
| cosine_accuracy@5 | 0.8743 | |
|
| cosine_accuracy@10 | 0.9114 | |
|
| cosine_precision@1 | 0.7071 | |
|
| cosine_precision@3 | 0.281 | |
|
| cosine_precision@5 | 0.1749 | |
|
| cosine_precision@10 | 0.0911 | |
|
| cosine_recall@1 | 0.7071 | |
|
| cosine_recall@3 | 0.8429 | |
|
| cosine_recall@5 | 0.8743 | |
|
| cosine_recall@10 | 0.9114 | |
|
| cosine_ndcg@10 | 0.8127 | |
|
| cosine_mrr@10 | 0.7807 | |
|
| **cosine_map@100** | **0.7841** | |
|
|
|
#### 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 | 0.7029 | |
|
| cosine_accuracy@3 | 0.8357 | |
|
| cosine_accuracy@5 | 0.8686 | |
|
| cosine_accuracy@10 | 0.9071 | |
|
| cosine_precision@1 | 0.7029 | |
|
| cosine_precision@3 | 0.2786 | |
|
| cosine_precision@5 | 0.1737 | |
|
| cosine_precision@10 | 0.0907 | |
|
| cosine_recall@1 | 0.7029 | |
|
| cosine_recall@3 | 0.8357 | |
|
| cosine_recall@5 | 0.8686 | |
|
| cosine_recall@10 | 0.9071 | |
|
| cosine_ndcg@10 | 0.8087 | |
|
| cosine_mrr@10 | 0.7769 | |
|
| **cosine_map@100** | **0.7806** | |
|
|
|
#### 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 | 0.6914 | |
|
| cosine_accuracy@3 | 0.82 | |
|
| cosine_accuracy@5 | 0.8557 | |
|
| cosine_accuracy@10 | 0.9014 | |
|
| cosine_precision@1 | 0.6914 | |
|
| cosine_precision@3 | 0.2733 | |
|
| cosine_precision@5 | 0.1711 | |
|
| cosine_precision@10 | 0.0901 | |
|
| cosine_recall@1 | 0.6914 | |
|
| cosine_recall@3 | 0.82 | |
|
| cosine_recall@5 | 0.8557 | |
|
| cosine_recall@10 | 0.9014 | |
|
| cosine_ndcg@10 | 0.7981 | |
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| cosine_mrr@10 | 0.765 | |
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| **cosine_map@100** | **0.7689** | |
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#### Information Retrieval |
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* Dataset: `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.6543 | |
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| cosine_accuracy@3 | 0.7886 | |
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| cosine_accuracy@5 | 0.8329 | |
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| cosine_accuracy@10 | 0.8829 | |
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| cosine_precision@1 | 0.6543 | |
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| cosine_precision@3 | 0.2629 | |
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| cosine_precision@5 | 0.1666 | |
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| cosine_precision@10 | 0.0883 | |
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| cosine_recall@1 | 0.6543 | |
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| cosine_recall@3 | 0.7886 | |
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| cosine_recall@5 | 0.8329 | |
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| cosine_recall@10 | 0.8829 | |
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| cosine_ndcg@10 | 0.769 | |
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| cosine_mrr@10 | 0.7325 | |
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| **cosine_map@100** | **0.7369** | |
|
|
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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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### Recommendations |
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## Training Details |
|
|
|
### Training Dataset |
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|
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#### Unnamed Dataset |
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|
|
|
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* Size: 6,300 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 10 tokens</li><li>mean: 46.55 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.56 tokens</li><li>max: 42 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Internationally, Visa Inc.'s commercial payments volume grew by 23% from $407 billion in 2021 to $500 billion in 2022.</code> | <code>What was the growth rate of Visa Inc.'s commercial payments volume internationally between 2021 and 2022?</code> | |
|
| <code>The consolidated financial statements and accompanying notes listed in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included immediately following Part IV hereof.</code> | <code>Where can one find the consolidated financial statements and accompanying notes in the Annual Report on Form 10-K?</code> | |
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| <code>The additional paid-in capital at the end of 2023 was recorded as $114,519 million.</code> | <code>What was the amount recorded for additional paid-in capital at the end of 2023?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```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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|
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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`: 80 |
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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`: 15 |
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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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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 80 |
|
- `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 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 15 |
|
- `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 |
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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`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
|
- `load_best_model_at_end`: False |
|
- `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} |
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- `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 |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:-------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.8101 | 4 | - | 0.7066 | 0.7309 | 0.7390 | 0.6462 | 0.7441 | |
|
| 1.8228 | 9 | - | 0.7394 | 0.7497 | 0.7630 | 0.6922 | 0.7650 | |
|
| 2.0253 | 10 | 2.768 | - | - | - | - | - | |
|
| 2.8354 | 14 | - | 0.7502 | 0.7625 | 0.7767 | 0.7208 | 0.7787 | |
|
| 3.8481 | 19 | - | 0.7553 | 0.7714 | 0.7804 | 0.7234 | 0.7802 | |
|
| 4.0506 | 20 | 1.1294 | - | - | - | - | - | |
|
| 4.8608 | 24 | - | 0.7577 | 0.7769 | 0.7831 | 0.7327 | 0.7858 | |
|
| 5.8734 | 29 | - | 0.7616 | 0.7775 | 0.7832 | 0.7335 | 0.7876 | |
|
| 6.0759 | 30 | 0.7536 | - | - | - | - | - | |
|
| 6.8861 | 34 | - | 0.7624 | 0.7788 | 0.7832 | 0.7352 | 0.7882 | |
|
| 7.8987 | 39 | - | 0.7665 | 0.7795 | 0.7814 | 0.7359 | 0.7861 | |
|
| 8.1013 | 40 | 0.5846 | - | - | - | - | - | |
|
| 8.9114 | 44 | - | 0.7688 | 0.7801 | 0.7828 | 0.7360 | 0.7857 | |
|
| 9.9241 | 49 | - | 0.7698 | 0.7804 | 0.7836 | 0.7367 | 0.7840 | |
|
| 10.1266 | 50 | 0.5187 | - | - | - | - | - | |
|
| 10.9367 | 54 | - | 0.7692 | 0.7801 | 0.7827 | 0.7383 | 0.7837 | |
|
| 11.9494 | 59 | - | 0.7698 | 0.7801 | 0.7834 | 0.7377 | 0.7849 | |
|
| 12.1519 | 60 | 0.4949 | 0.7689 | 0.7806 | 0.7841 | 0.7369 | 0.7839 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.2.0+cu121 |
|
- Accelerate: 0.31.0 |
|
- 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} |
|
} |
|
``` |
|
|
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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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