setfit safetensors bert sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 model-index region:us
---
base_model: BAAI/bge-base-en-v1.5
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'The answer provided concisely lists the changes being made to the storage
    AM as per Haribabu Kommi''s email. It addresses the specific question asked and
    is directly related to the content of the provided document. There is no deviation
    into unrelated topics, and unnecessary information is avoided.


    Final Evaluation:'
- text: 'Reasoning:

    1. Context Grounding: The provided document clearly states that China''s Ning
    Zhongyan won the gold medal in the men''s 1,500m final at the speed skating World
    Cup.

    2. Relevance: The answer directly addresses who won the gold medal in the men''s
    1,500m final at the speed skating World Cup, which is the specific question asked.

    3. Conciseness: The answer is short and to the point, providing just the necessary
    information without any unnecessary elaboration.


    Final result:'
- text: 'Evaluation:


    1. **Context Grounding:** The answer correctly states the sizes for both individual
    and combined portraits as listed in the provided document.

    2. **Relevance:** The answer directly addresses the specific question regarding
    the sizes of the portraits available.

    3. **Conciseness:** The answer is clear, to the point, and avoids unnecessary
    information, making it concise.


    Final result:'
- text: "The provided answer is well-supported by the document and directly related\
    \ to the question. It enumerates the components of the British Medieval Student\
    \ Guide accurately. \n\nReasoning:\n1. Context Grounding: The answer is consistent\
    \ with the provided document, which lists the elements of the Student Guide.\n\
    2. Relevance: The answer directly addresses the question asked, providing detailed\
    \ components of the guide.\n3. Conciseness: The answer is clear and to the point,\
    \ avoiding unnecessary information that doesn't pertain to the question.\n\nFinal\
    \ Evaluation:"
- text: 'The answer lists Rep. Andy Harris, Reps. Kyle Evans, and Jessica Smith as
    the first three Members of Congress to call for an end to the blockade of Gaza.
    However, according to the document, the correct individuals are Reps. Keith Ellison,
    Barbara Lee, and Danny Davis. Therefore, the answer is not grounded in the provided
    document and is factually incorrect.


    The final evaluation:'
inference: true
model-index:
- name: SetFit with BAAI/bge-base-en-v1.5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.88
      name: Accuracy
---

# SetFit with BAAI/bge-base-en-v1.5

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
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<!-- - **Language:** Unknown -->
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### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

### Model Labels
| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>"The answer correctly identifies that the primary reasons behind the Nuggets' offensive outburst in January are the team's increased comfort and effectiveness, as well as Coach Brian Shaw's strategy of encouraging the team to push the ball after makes and misses and to take the first available shot in the rhythm of the offense. However, the mention of a new training technique involving virtual reality is not supported by the provided document.\n\nReasoning:\n1. Context Grounding: The majority of the answer is well-supported by the document, but the part about virtual reality training is not mentioned in the provided text.\n2. Relevance: The answer is mostly relevant to the question, but the inclusion of virtual reality training deviates from the information in the document.\n3. Conciseness: The answer could be clearer and more concise by excluding the irrelevant information about virtual reality training.\n\nThe final evaluation:"</li><li>"**Reasoning:**\n\n1. **Context Grounding:** The answer is generally well-grounded in the document but contains some inaccuracies. The document discusses that film over-exposes better, not under-exposes better. The answer also mentions 5MP sensors, while the document refers to 10MP. \n\n2. **Relevance:** The answer is relevant to the question, addressing the differences between film and digital photography based on the author's experience.\n\n3. **Conciseness:** The answer is concise and to the point, which is good. However, inaccuracies in the details affect its quality.\n\n**Final Result:**"</li><li>'Reasoning:\nThe provided answer does not address the question asked. The question seeks information about the main conflict in the third book of the Arcana Chronicles by Kresley Cole, while the answer given only discusses the results of a mixed martial arts event and the performance of fighters in various bouts. This answer is neither relevant to the question nor grounded in the correct context.\n\nFinal evaluation:'</li></ul> |
| 1     | <ul><li>'The answer provided effectively outlines best practices for web designers, detailing practices such as understanding client needs, signing detailed contracts, and maintaining clear communication. These are directly rooted in the provided document and address the specified question accurately.\n\n1. **Context Grounding:** \n   - The answer is well-supported by the document, specifically referencing getting to know the client, maintaining a contract, and explaining the importance of communication as outlined in the text.\n\n2. **Relevance:**\n   - The answer is highly relevant to the question, focusing precisely on best practices for web designers to avoid unnecessary revisions and conflicts.\n\n3. **Conciseness:**\n   - The answer is clear, concise, and avoids extraneous details.\n\nFinal evaluation:'</li><li>"Reasoning:\n\n1. **Context Grounding**: The answer is well-supported by the provided document. The author does emphasize that using the author's own experiences, especially those involving pain and emotion, makes the story genuine and relatable, thereby creating a connection between the reader and the characters.\n \n2. **Relevance**: The answer directly addresses the specific question asked about the key to creating a connection between the reader and the characters in a story.\n\n3. **Conciseness**: The answer is clear and to the point, without including unnecessary information.\n\nFinal result:"</li><li>'Reasoning:\n1. Context Grounding: The answer is directly supported by the provided document, which mentions that Mauro Rubin is the CEO of JoinPad and that he spoke during the event at Talent Garden Calabiana, Milan.\n2. Relevance: The answer is relevant to the question asked, directly addressing the identity of the CEO during the specified event.\n3. Conciseness: The answer is clear, to the point, and does not include unnecessary information.\n\nFinal result:'</li></ul>                                                                                  |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.88     |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Netta1994/setfit_baai_rag_ds_gpt-4o_cot-few_shot-instructions_remove_final_evaluation_e1_172700")
# Run inference
preds = model("The answer provided concisely lists the changes being made to the storage AM as per Haribabu Kommi's email. It addresses the specific question asked and is directly related to the content of the provided document. There is no deviation into unrelated topics, and unnecessary information is avoided.

Final Evaluation:")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 28  | 79.3803 | 155 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 34                    |
| 1     | 37                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0056 | 1    | 0.2384        | -               |
| 0.2809 | 50   | 0.2527        | -               |
| 0.5618 | 100  | 0.1556        | -               |
| 0.8427 | 150  | 0.0404        | -               |

### Framework Versions
- Python: 3.10.14
- SetFit: 1.1.0
- Sentence Transformers: 3.1.1
- Transformers: 4.44.0
- PyTorch: 2.4.0+cu121
- Datasets: 3.0.0
- Tokenizers: 0.19.1

## Citation

### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
```

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