sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:178 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
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
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:178
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Where can investors find more information about NVIDIA's financial
    information and company updates?
  sentences:
  - ' The potential risks include restrictions on sales of products containing certain
    components made by Micron, restrictions on receiving supply of components, parts,
    or services from Taiwan, increased scrutiny from shareholders, regulators, and
    others regarding corporate sustainability practices, and failure to meet evolving
    shareholder, regulator, or other industry stakeholder expectations, which could
    result in additional costs, reputational harm, and loss of customers and suppliers.'
  - ' Investors and others can find more information about NVIDIA''s financial information
    and company updates on the company''s investor relations website, through press
    releases, SEC filings, public conference calls and webcasts, as well as on the
    company''s social media channels, including Twitter, the NVIDIA Corporate Blog,
    Facebook, LinkedIn, Instagram, and YouTube.'
  - ' The text mentions the following forms and agreements: Officers'' Certificate,
    Form of Note (with various years), Form of Indemnity Agreement, Amended and Restated
    2007 Equity Incentive Plan, Non-Employee Director Deferred Restricted Stock Unit
    Grant Notice and Deferred Restricted Stock Unit Agreement, Non-Employee Director
    Restricted Stock Unit Grant Notice and Restricted Stock Unit Agreement, Global
    Performance-Based Restricted Stock Unit Grant Notice and Performance-Based Restricted
    Stock Unit Agreement, Global Restricted Stock Unit Grant Notice and Global Restricted
    Stock Unit Agreement, and various Schedules and Exhibits (such as 2.1, 3.1, 4.1,
    4.2, 10.1, 10.2, 10.26, and 10.27).'
- source_sentence: What are the potential consequences if regulators in China conclude
    that NVIDIA has failed to fulfill its commitments or has violated applicable law
    in China?
  sentences:
  - ' The company''s share repurchase program aims to offset dilution from shares
    issued to employees.'
  - ' Ms. Shoquist served as Senior Vice President and General Manager of the Electro-Optics
    business at Coherent, Inc., and previously worked at Quantum Corp. as President
    of the Personal Computer Hard Disk Drive Division, and at Hewlett-Packard.'
  - ' If regulators in China conclude that NVIDIA has failed to fulfill its commitments
    or has violated applicable law in China, the company could be subject to various
    penalties or restrictions on its ability to conduct its business, which could
    have a material and adverse impact on its business, operating results, and financial
    condition.'
- source_sentence: What percentage of the company's revenue was attributed to sales
    to customers outside of the United States in fiscal year 2024?
  sentences:
  - ' NVIDIA reports its business results in two segments: the Compute & Networking
    segment and the Graphics segment.'
  - ' The company expects to use its existing cash, cash equivalents, and marketable
    securities, as well as the cash generated by its operations, to fund its capital
    investments of approximately $3.5 billion to $4.0 billion related to property
    and equipment during fiscal year 2025.'
  - ' 56% of the company''s total revenue in fiscal year 2024 was attributed to sales
    to customers outside of the United States.'
- source_sentence: What is the net income per share of NVIDIA Corporation for the
    year ended January 29, 2023?
  sentences:
  - ' 6% of the company''s workforce in the United States is composed of Black or
    African American employees.'
  - ' The net income per share of NVIDIA Corporation for the year ended January 29,
    2023 is $12.05 for basic and $11.93 for diluted.'
  - ' The company may face potential risks and challenges such as increased expenses,
    substantial expenditures and time spent to fully resume operations, disruption
    to product development or operations due to employees being called-up for active
    military duty, and potential impact on future product development, operations,
    and revenue. Additionally, the company may also experience interruptions or delays
    in services from third-party providers, which could impair its ability to provide
    its products and services and harm its business.'
- source_sentence: What percentage of the company's accounts receivable balance as
    of January 28, 2024, was accounted for by two customers?
  sentences:
  - ' The change in equipment and assembly and test equipment resulted in a benefit
    of $135 million in operating income and $114 million in net income, or $0.05 per
    both basic and diluted share, for the fiscal year ended January 28, 2024.'
  - ' The estimates of deferred tax assets and liabilities may change based on added
    certainty or finality to an anticipated outcome, changes in accounting standards
    or tax laws in the U.S. or foreign jurisdictions where the company operates, or
    changes in other facts or circumstances.'
  - ' 24% and 11%, which is a total of 35%.'
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: bge base en
      type: bge-base-en
    metrics:
    - type: cosine_accuracy@1
      value: 0.9269662921348315
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9831460674157303
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9943820224719101
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9269662921348315
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3277153558052434
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.198876404494382
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9269662921348315
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9831460674157303
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9943820224719101
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9682702490705566
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9575842696629214
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9575842696629213
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.9269662921348315
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.9831460674157303
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.9943820224719101
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 1.0
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.9269662921348315
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.3277153558052434
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.198876404494382
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.09999999999999998
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.9269662921348315
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.9831460674157303
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.9943820224719101
      name: Dot Recall@5
    - type: dot_recall@10
      value: 1.0
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.9682702490705566
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.9575842696629214
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.9575842696629213
      name: Dot Map@100
---

# SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the train dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - train
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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("rezarahim/bge-finetuned-detail")
# Run inference
sentences = [
    "What percentage of the company's accounts receivable balance as of January 28, 2024, was accounted for by two customers?",
    ' 24% and 11%, which is a total of 35%.',
    ' The change in equipment and assembly and test equipment resulted in a benefit of $135 million in operating income and $114 million in net income, or $0.05 per both basic and diluted share, for the fiscal year ended January 28, 2024.',
]
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]
```

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</details>
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You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `bge-base-en`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.927      |
| cosine_accuracy@3   | 0.9831     |
| cosine_accuracy@5   | 0.9944     |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.927      |
| cosine_precision@3  | 0.3277     |
| cosine_precision@5  | 0.1989     |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.927      |
| cosine_recall@3     | 0.9831     |
| cosine_recall@5     | 0.9944     |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9683     |
| cosine_mrr@10       | 0.9576     |
| **cosine_map@100**  | **0.9576** |
| dot_accuracy@1      | 0.927      |
| dot_accuracy@3      | 0.9831     |
| dot_accuracy@5      | 0.9944     |
| dot_accuracy@10     | 1.0        |
| dot_precision@1     | 0.927      |
| dot_precision@3     | 0.3277     |
| dot_precision@5     | 0.1989     |
| dot_precision@10    | 0.1        |
| dot_recall@1        | 0.927      |
| dot_recall@3        | 0.9831     |
| dot_recall@5        | 0.9944     |
| dot_recall@10       | 1.0        |
| dot_ndcg@10         | 0.9683     |
| dot_mrr@10          | 0.9576     |
| dot_map@100         | 0.9576     |

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## Training Details

### Training Dataset

#### train

* Dataset: train
* Size: 178 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 178 samples:
  |         | anchor                                                                             | positive                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 23.63 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 66.67 tokens</li><li>max: 313 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                                                      | positive                                                                                                                                                                                                                                                                                                                   |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What is the publication date of the NVIDIA Corporation Annual Report 2024?</code>                                                                                                                                                     | <code> The publication date of the NVIDIA Corporation Annual Report 2024 is February 21st, 2024.</code>                                                                                                                                                                                                                    |
  | <code>What is the filing date of the 10-K report for NVIDIA Corporation in 2004?</code>                                                                                                                                                     | <code> The filing dates of the 10-K reports for NVIDIA Corporation in 2004 are May 20th, March 29th, and April 25th.</code>                                                                                                                                                                                                |
  | <code>What is the purpose of the section of the filing that requires the registrant to indicate whether it has submitted electronically every Interactive Data File required to be submitted pursuant to Rule 405 of Regulation S-T?</code> | <code> The purpose of this section is to require the registrant to disclose whether it has submitted all required Interactive Data Files electronically, as mandated by Rule 405 of Regulation S-T, during the preceding 12 months or for the shorter period that the registrant was required to submit such files.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 25
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `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`: 4
- `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`: 25
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: 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`: 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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch       | Step   | Training Loss | bge-base-en_cosine_map@100 |
|:-----------:|:------:|:-------------:|:--------------------------:|
| 0           | 0      | -             | 0.8574                     |
| 0.7111      | 2      | -             | 0.8591                     |
| 1.7778      | 5      | -             | 0.8757                     |
| 2.8444      | 8      | -             | 0.9012                     |
| 3.5556      | 10     | 0.2885        | -                          |
| 3.9111      | 11     | -             | 0.9134                     |
| 4.9778      | 14     | -             | 0.9277                     |
| 5.6889      | 16     | -             | 0.9391                     |
| 6.7556      | 19     | -             | 0.9463                     |
| 7.1111      | 20     | 0.0644        | -                          |
| 7.8222      | 22     | -             | 0.9506                     |
| 8.8889      | 25     | -             | 0.9515                     |
| 9.9556      | 28     | -             | 0.9555                     |
| 10.6667     | 30     | 0.0333        | 0.9560                     |
| 11.7333     | 33     | -             | 0.9551                     |
| 12.8        | 36     | -             | 0.9569                     |
| **13.8667** | **39** | **-**         | **0.9579**                 |
| 14.2222     | 40     | 0.0157        | -                          |
| 14.9333     | 42     | -             | 0.9576                     |
| 16.0        | 45     | -             | 0.9576                     |
| 16.7111     | 47     | -             | 0.9576                     |
| 17.7778     | 50     | 0.0124        | 0.9576                     |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3

## 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",
}
```

#### 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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