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:

    The answer provided is correct, closely aligned with the detailed steps and descriptions
    provided in the document. It covers key identification points such as body color,
    shiny carapace, large fangs, and caution required due to the spider''s venomous
    nature.


    Evaluation:'
- text: 'Reasoning:

    Information is accurate and matches content from the provided document.

    Evaluation:'
- text: 'Reasoning:

    The answer correctly outlines important steps towards getting into medical school,
    although it doesn''t cover the complete range of advice given in the document,
    it does contain many relevant points from the Document. It covers topics such
    as getting good grades in prerequisite courses, gaining clinical experience, and
    preparing for the MCAT, aligning with the document''s advice.


    However, some recommendations are overly narrow (e.g., suggesting volunteering
    at Planned Parenthood specifically) and introducing the practice of applying plaster,
    which the document doesn''t mention or discourage.


    Evaluation:'
- text: 'Reasoning:

    The answer aligns closely with the document provided. It offers practical and
    creative hiding strategies that are substantiated by the document. The tips about
    hiding in nearby corners, utilizing multi-colored areas for camouflage, and hiding
    in plain sight are mentioned in the document. Moreover, it avoids recommending
    dangerous hiding spots and emphasizes the importance of coming up with clever
    hiding places.

    Evaluation:'
- text: 'Reasoning:

    The information provided in the answer is incorrect.

    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: 0.7466666666666667
      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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'Reasoning:\nirrelevant - The answer is not relevant to what is being asked.\nEvaluation:'</li><li>'Reasoning:\nWhile the provided answer accurately identifies multiple services offered by Kartz Media & PR, it introduces a fictional service: "personalized space travel public relations for interstellar companies," which is not mentioned in the document. The remaining services listed in the answer align well with the services described in the provided document.\n\nEvaluation:'</li><li>'Reasoning:\nirrelevant - The answer provided does not address the question, and the informationis not relevant to what is asked.\nEvaluation:'</li></ul> |
| 1     | <ul><li>'Reasoning:\nThe answer is accurate and directly taken from the relevant part of the document. It correctly identifies Open Data and standard formats like XML, JSON,or CSV as the proposed solution.\nEvaluation:'</li><li>'Reasoning:\nclearly correct - The answer completely and accurately addresses the question. The steps for using plastic wrap and straws are detailed and align with the instructions provided in the document.\n\nEvaluation:'</li><li>'Reasoning:\ngood - The answer accurately corresponds to the details provided in the document about the value of a dime.\nEvaluation:'</li></ul>                                               |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7467   |

## 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_wikisum_gpt-4o_improved-cot_chat_few_shot_remove_final_evaluation_e1_larg")
# Run inference
preds = model("Reasoning:
The information provided in the answer is incorrect.
Evaluation:")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 6   | 33.1685 | 156 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 82                    |
| 1     | 102                   |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0022 | 1    | 0.2065        | -               |
| 0.1087 | 50   | 0.2382        | -               |
| 0.2174 | 100  | 0.1573        | -               |
| 0.3261 | 150  | 0.0988        | -               |
| 0.4348 | 200  | 0.029         | -               |
| 0.5435 | 250  | 0.012         | -               |
| 0.6522 | 300  | 0.0105        | -               |
| 0.7609 | 350  | 0.0136        | -               |
| 0.8696 | 400  | 0.0178        | -               |
| 0.9783 | 450  | 0.0091        | -               |

### 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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