|
--- |
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base_model: BAAI/bge-base-en-v1.5 |
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datasets: [] |
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language: |
|
- 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 |
|
- 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 |
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- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
|
- generated_from_trainer |
|
- 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: Total net additions to property and equipment for AWS in 2023 amounted |
|
to $24,843 million. |
|
sentences: |
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- What technological feature helps protect digital transactions in the Visa Token |
|
Service? |
|
- What was the total net addition to property and equipment for AWS in the year |
|
2023? |
|
- By what proportion did net cash used in financing activities increase from 2022 |
|
to 2023? |
|
- source_sentence: 'Leases generally contain one or more of the following options, |
|
which the Company can exercise at the end of the initial term: (a) renew the lease |
|
for a defined number of years at the then-fair market rental rate or rate stipulated |
|
in the lease agreement; (b) purchase the property at the then-fair market value |
|
or purchase price stated in the agreement; or (c) a right of first refusal in |
|
the event of a third-party offer.' |
|
sentences: |
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- What are the requirements for health insurers and group health plans in providing |
|
cost estimates to consumers? |
|
- What options does the company have at the end of the lease term for their leased |
|
properties? |
|
- How much did the company incur in intangible amortization costs related to the |
|
eOne acquisition in 2022? |
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- source_sentence: We recorded an acquisition termination cost of $1.35 billion in |
|
fiscal year 2023 reflecting the write-off of the prepayment provided at signing. |
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sentences: |
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- How much did NVIDIA record as an acquisition termination cost in fiscal year 2023 |
|
related to the Arm Share Purchase Agreement? |
|
- What is included in the consolidated financial statements and accompanying notes |
|
mentioned in Part IV, Item 15(a)(1) of the Annual Report on Form 10-K? |
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- What risks are associated with projecting the effectiveness of internal controls |
|
into future periods as mentioned? |
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- source_sentence: Item 8 is labeled as Financial Statements and Supplementary Data. |
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sentences: |
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- What was the percentage of trading days in 2023 where trading-related revenue |
|
was recorded as positive? |
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- How is the discount rate for the Family Dollar goodwill impairment evaluation |
|
determined? |
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- What is the title of Item 8 in the financial document? |
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- source_sentence: Details about legal proceedings are included in Part II, Item 8, |
|
"Financial Statements and Supplementary Data" of the Annual Report on Form 10-K, |
|
under the caption "Legal Proceedings". |
|
sentences: |
|
- Where can details about legal proceedings be located in an Annual Report on Form |
|
10-K? |
|
- How many stores did AutoZone operate in the United States as of August 26, 2023? |
|
- In the context of Hewlett Packard Enterprise's recent financial discussions, what |
|
factors are expected to impact their operational costs and revenue growth moving |
|
forward? |
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model-index: |
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- name: BGE base Financial Matryoshka |
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results: |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7071428571428572 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8414285714285714 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.88 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9314285714285714 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7071428571428572 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.28047619047619043 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.176 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09314285714285712 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7071428571428572 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8414285714285714 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.88 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9314285714285714 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8207437059171859 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7853486394557823 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7881907906804949 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6957142857142857 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8385714285714285 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8757142857142857 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.93 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6957142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2795238095238095 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17514285714285713 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09299999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6957142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8385714285714285 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8757142857142857 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.93 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8149439460863356 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7780714285714285 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.781021025356189 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6885714285714286 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.83 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8742857142857143 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9142857142857143 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6885714285714286 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.27666666666666667 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17485714285714282 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09142857142857141 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6885714285714286 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.83 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8742857142857143 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9142857142857143 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8060991379418679 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7710873015873015 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7751792513774886 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6771428571428572 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8214285714285714 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8614285714285714 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9142857142857143 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6771428571428572 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2738095238095238 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17228571428571426 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09142857142857141 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6771428571428572 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8214285714285714 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8614285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9142857142857143 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7979494993398927 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7605890022675734 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7639633810343436 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6557142857142857 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7871428571428571 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8271428571428572 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8714285714285714 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6557142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2623809523809524 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1654285714285714 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08714285714285713 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6557142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7871428571428571 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8271428571428572 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8714285714285714 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7664083634078753 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7326604308390022 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7375736792740525 |
|
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("dustyatx/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
|
'Details about legal proceedings are included in Part II, Item 8, "Financial Statements and Supplementary Data" of the Annual Report on Form 10-K, under the caption "Legal Proceedings".', |
|
'Where can details about legal proceedings be located in an Annual Report on Form 10-K?', |
|
'How many stores did AutoZone operate in the United States as of August 26, 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 |
|
* 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 | 0.7071 | |
|
| cosine_accuracy@3 | 0.8414 | |
|
| cosine_accuracy@5 | 0.88 | |
|
| cosine_accuracy@10 | 0.9314 | |
|
| cosine_precision@1 | 0.7071 | |
|
| cosine_precision@3 | 0.2805 | |
|
| cosine_precision@5 | 0.176 | |
|
| cosine_precision@10 | 0.0931 | |
|
| cosine_recall@1 | 0.7071 | |
|
| cosine_recall@3 | 0.8414 | |
|
| cosine_recall@5 | 0.88 | |
|
| cosine_recall@10 | 0.9314 | |
|
| cosine_ndcg@10 | 0.8207 | |
|
| cosine_mrr@10 | 0.7853 | |
|
| **cosine_map@100** | **0.7882** | |
|
|
|
#### 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 | 0.6957 | |
|
| cosine_accuracy@3 | 0.8386 | |
|
| cosine_accuracy@5 | 0.8757 | |
|
| cosine_accuracy@10 | 0.93 | |
|
| cosine_precision@1 | 0.6957 | |
|
| cosine_precision@3 | 0.2795 | |
|
| cosine_precision@5 | 0.1751 | |
|
| cosine_precision@10 | 0.093 | |
|
| cosine_recall@1 | 0.6957 | |
|
| cosine_recall@3 | 0.8386 | |
|
| cosine_recall@5 | 0.8757 | |
|
| cosine_recall@10 | 0.93 | |
|
| cosine_ndcg@10 | 0.8149 | |
|
| cosine_mrr@10 | 0.7781 | |
|
| **cosine_map@100** | **0.781** | |
|
|
|
#### 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 | 0.6886 | |
|
| cosine_accuracy@3 | 0.83 | |
|
| cosine_accuracy@5 | 0.8743 | |
|
| cosine_accuracy@10 | 0.9143 | |
|
| cosine_precision@1 | 0.6886 | |
|
| cosine_precision@3 | 0.2767 | |
|
| cosine_precision@5 | 0.1749 | |
|
| cosine_precision@10 | 0.0914 | |
|
| cosine_recall@1 | 0.6886 | |
|
| cosine_recall@3 | 0.83 | |
|
| cosine_recall@5 | 0.8743 | |
|
| cosine_recall@10 | 0.9143 | |
|
| cosine_ndcg@10 | 0.8061 | |
|
| cosine_mrr@10 | 0.7711 | |
|
| **cosine_map@100** | **0.7752** | |
|
|
|
#### 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 | 0.6771 | |
|
| cosine_accuracy@3 | 0.8214 | |
|
| cosine_accuracy@5 | 0.8614 | |
|
| cosine_accuracy@10 | 0.9143 | |
|
| cosine_precision@1 | 0.6771 | |
|
| cosine_precision@3 | 0.2738 | |
|
| cosine_precision@5 | 0.1723 | |
|
| cosine_precision@10 | 0.0914 | |
|
| cosine_recall@1 | 0.6771 | |
|
| cosine_recall@3 | 0.8214 | |
|
| cosine_recall@5 | 0.8614 | |
|
| cosine_recall@10 | 0.9143 | |
|
| cosine_ndcg@10 | 0.7979 | |
|
| cosine_mrr@10 | 0.7606 | |
|
| **cosine_map@100** | **0.764** | |
|
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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) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.6557 | |
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| cosine_accuracy@3 | 0.7871 | |
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| cosine_accuracy@5 | 0.8271 | |
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| cosine_accuracy@10 | 0.8714 | |
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| cosine_precision@1 | 0.6557 | |
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| cosine_precision@3 | 0.2624 | |
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| cosine_precision@5 | 0.1654 | |
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| cosine_precision@10 | 0.0871 | |
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| cosine_recall@1 | 0.6557 | |
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| cosine_recall@3 | 0.7871 | |
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| cosine_recall@5 | 0.8271 | |
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| cosine_recall@10 | 0.8714 | |
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| cosine_ndcg@10 | 0.7664 | |
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| cosine_mrr@10 | 0.7327 | |
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| **cosine_map@100** | **0.7376** | |
|
|
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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|
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## Training Details |
|
|
|
### Training Dataset |
|
|
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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: 8 tokens</li><li>mean: 45.94 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.7 tokens</li><li>max: 42 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>The company must continuously strengthen its capabilities in marketing and innovation to compete in a digital environment and maintain brand loyalty and marketallability. In addition, it is increasing its investments in e-commerce to support retail and meal delivery services, offering more package sizes that are fit-for-purpose for online sales and shifting more consumer and trade promotions to digital.</code> | <code>What strategies is the company employing to enhance its competitiveness in a digital environment?</code> | |
|
| <code>Fedflowing expanded or relocated its hub and linehaul network, FedEx Ground also introduced new safety technologies, set new driver standards, and made operational enhancements for safer handling of heavy items.</code> | <code>What specific changes has FedEx Ground made for vehicle and driver safety?</code> | |
|
| <code>The debt financing, which is being provided by a syndicate of Chinese financial institutions, contains certain covenants and a maximum borrowing limit of ¥29.7 billion RMB (approximately $4.2 billion).</code> | <code>What is the maximum borrowing limit of the debt financing provided by the syndicate of Chinese financial institutions for Universal Beijing Resort?</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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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
|
- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
|
|
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#### All Hyperparameters |
|
<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 |
|
- `torch_empty_cache_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 4 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: True |
|
- `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 |
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- `label_names`: None |
|
- `load_best_model_at_end`: True |
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- `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 |
|
- `eval_on_start`: False |
|
- `eval_use_gather_object`: 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 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.8122 | 10 | 1.5212 | - | - | - | - | - | |
|
| 0.9746 | 12 | - | 0.7439 | 0.7556 | 0.7670 | 0.7142 | 0.7717 | |
|
| 1.6244 | 20 | 0.6418 | - | - | - | - | - | |
|
| 1.9492 | 24 | - | 0.7592 | 0.7743 | 0.7787 | 0.7331 | 0.7839 | |
|
| 2.4365 | 30 | 0.4411 | - | - | - | - | - | |
|
| 2.9239 | 36 | - | 0.7623 | 0.7757 | 0.7816 | 0.7365 | 0.7902 | |
|
| 3.2487 | 40 | 0.3917 | - | - | - | - | - | |
|
| **3.8985** | **48** | **-** | **0.764** | **0.7752** | **0.781** | **0.7376** | **0.7882** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.11.9 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.0+cu121 |
|
- Accelerate: 0.33.0 |
|
- Datasets: 2.21.0 |
|
- 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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*Clearly define terms in order to be accessible across audiences.* |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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