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: "Reasoning:\nThe provided answer is accurate and directly addresses the question\
    \ by listing the specific changes Haribabu Kommi is making to the storage AM as\
    \ detailed in his email. The changes include support for the storage method in\
    \ the `create access method` syntax, adding necessary storage AM API hooks, updating\
    \ the relation structure with a storageAm routine pointer, moving tuple visibility\
    \ functions to heap storage AM, adding slot-specific storage AM routine pointers,\
    \ implementing write support functionality, and adding all scan-supported functions\
    \ to storage AM. Additionally, the potential need for further modifications due\
    \ to assumptions about the tuple format is mentioned. \n\nThe answer remains relevant\
    \ and concise, strictly sticking to the context given in the document.\n\nFinal\
    \ Evaluation:"
- text: 'Reasoning:

    The answer directly addresses the question by stating that China''s Ning Zhongyan
    won the gold medal in the men''s 1,500m final at the speed skating World Cup.
    This information is clearly found in the document, which confirms Ning''s achievement
    at the event in Stavanger, Norway. The answer is concise, relevant, and well-supported
    by the given context, avoiding extraneous details.


    Final Evaluation:'
- text: "Reasoning:\nThe provided answer directly addresses the specific question\
    \ regarding the available sizes for the individual and combined portraits of David\
    \ Bowie by Ian Davies. The sizes mentioned (30 x 28.5 inch and 40 x 38.5 inch\
    \ for individual portraits, and 60 inch x 30 inch and 80 inch x 40 inch for combined\
    \ portraits) are accurate and well-supported by the document. The answer remains\
    \ relevant, concise, and fully grounded in the context provided in the document\
    \ without deviating into unnecessarydetails.\n\nFinal Evaluation: \nEvaluation:"
- text: "Reasoning:\nThe answer provided is accurate and focuses on the components\
    \ of the British Medieval Student Guide as described in the document. It mentions\
    \ comprehension questions, writing exercises, discussion questions, an additional\
    \ reading list, semester and full-year schedules, and a bibliography. It also\
    \ correctly identifies the organization of the material into 4 units with 2 lessons\
    \ each, including Introduction, Comprehension Questions, Literary Lesson, and\
    \ Writing Exercises, along with an Appendix Section. These points are directly\
    \ supported by the information in the provided document.\n\nFinal Evaluation:\
    \ \nEvaluation:"
- text: 'Reasoning:

    The answer incorrectly names Rep. Andy Harris, Reps. Kyle Evans, and Jessica Smith
    as the first three Members of Congress calling for an end to the blockade of Gaza.
    The document clearly states that the first three Members of Congress to issue
    such public statements were Rep. Keith Ellison, Rep. Barbara Lee, and Rep. Danny
    Davis. The answer does not align with the provided information, indicating a lack
    of context grounding and relevance to the specific factual content required by
    the question.


    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: 0.9333333333333333
      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
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'Reasoning:\nThe answer correctly identifies Joan Gaspart as the individual who resigned from the presidency of Barcelona after the team\'s poor showing in the 2003 season. This is directly supported by the document, which explicitly states that "club president Joan Gaspart resigned, his position having been made completely untenable by such a disastrous season on top of the club\'s overall decline in fortunes since he became president three years prior." The answer is concise and directly relevant to the question without including any extraneous information.\n\nEvaluation:'</li><li>"Reasoning:\nThe provided answer directly addresses the question of why it is recommended to hire a professional residential electrician like O'Hara Electric for electrical work in your house. The answer highlights key points such as the hazards of working with electricity, the potential for injury, and the long-term implications of improperly done electrical work. It also mentions the risk involved even in seemingly simple tasks like smoke detector installation and emphasizes the benefits of having the job done correctly the first time by a professional. The details arewell-supported by the document.\n\nEvaluation:"</li><li>'Reasoning:\nThe answer "The title of Aerosmith\'s 1987 comeback album was \'Permanent Vacation\'" is directly supported by the provided document. The document explicitly states, "Aerosmith\'s comeback album Permanent Vacation (1987) would begin a decade long revival of their popularity." The answer is directly related to the question asked and does not deviate into unrelated topics, ensuring conciseness and relevance.\n\nEvaluation:'</li></ul>                                                       |
| 0     | <ul><li>'Reasoning:\nThe answer provides a well-supported response that aligns directly with the content presented in the document. It addresses various strategies to combat smoking cravings, such as identifying and avoiding triggers, using distractions, and engaging in alternative activities. Specific triggers, like daily routines and social situations, are described in both the answer and the document. Additionally, the advice on using chewing licorice root and engaging in smoke-free activities is related to the suggestions given in the document. The answer is clear, concise, and stays relevant to the question throughout.\n\nFinal Evaluation: \nEvaluation:'</li><li>"Reasoning:\nThe provided answer accurately captures the challenges Amy Bloom faces when starting a significant writing project, as detailed in the document. Notably, it mentions the difficulty of getting started, the need to clear mental space, and to recalibrate her daily life, which are all points grounded in the text. The answer also covers her becoming less involved in everyday life and spending less time on domestic concerns, which aligns well with the provided passage. However, the part about traveling to a remote island with no internet access is not mentioned in the document and appears to be fabricated, which detracts from the answer's context grounding.\n\nFinal Result:"</li><li>'Reasoning:\nThe provided answer incorrectly states the price and location of the 6 bedroom detached house. According to the document, the 6 bedroom detached house is for sale at a price of £950,000 and is located at Willow Drive, Twyford, Reading, Berkshire, RG10. The answer gives a different priceand an incorrect location.\n\nFinal Evaluation:'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9333   |

## 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_improved-cot-instructions_chat_few_shot_generated_remove_fi")
# Run inference
preds = model("Reasoning:
The answer directly addresses the question by stating that China's Ning Zhongyan won the gold medal in the men's 1,500m final at the speed skating World Cup. This information is clearly found in the document, which confirms Ning's achievement at the event in Stavanger, Norway. The answer is concise, relevant, and well-supported by the given context, avoiding extraneous details.

Final Evaluation:")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 33  | 76.9045 | 176 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 95                    |
| 1     | 104                   |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0020 | 1    | 0.2375        | -               |
| 0.1004 | 50   | 0.2548        | -               |
| 0.2008 | 100  | 0.2339        | -               |
| 0.3012 | 150  | 0.0973        | -               |
| 0.4016 | 200  | 0.0347        | -               |
| 0.5020 | 250  | 0.0125        | -               |
| 0.6024 | 300  | 0.0058        | -               |
| 0.7028 | 350  | 0.004         | -               |
| 0.8032 | 400  | 0.0033        | -               |
| 0.9036 | 450  | 0.0023        | -               |

### 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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