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: 0.8266666666666667
      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:\nhallucination - The answer contains information not found in the document, which indicates that it is hallucinating.\nEvaluation:'</li><li>'Reasoning:\nincomplete - The answer does not cover all the essential details found in the document.\nEvaluation:'</li><li>'Reasoning:\nirrelevant - The answer is not relevant to what is being asked.\nEvaluation:'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
| 1     | <ul><li>'Reasoning:\nThe answer directly addresses the question of how to hold a note and covers techniques that are explained in the document. It mentions breathing steadily, engaging abdominal muscles, and maintaining good posture—all crucial points found in the provided document. \n\nEvaluation:'</li><li>'Reasoning:\nThe answer corresponds well with the suggestions and concepts presented in the document, such as journaling, trying new activities, and engaging in social interactions, which are crucial for addressing feelings of emptiness.\nEvaluation:'</li><li>'Reasoning:\ngoodThe answer aligns well with the given information from the document, specifically mentioning key steps such as gently squeezing out excess water using hands, applying a leave-in conditioner to prevent frizz, detangling with a wide-tooth comb, and adding styling products. The answer is accurate, relevant, and comprehensive.\n\nEvaluation:'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8267   |

## 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_1726")
# 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   | 32.6056 | 156 |

| 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    | 0.2269        | -               |
| 0.2809 | 50   | 0.2147        | -               |
| 0.5618 | 100  | 0.098         | -               |
| 0.8427 | 150  | 0.025         | -               |

### Framework Versions
- Python: 3.10.14
- SetFit: 1.1.0
- Sentence Transformers: 3.1.0
- Transformers: 4.44.0
- PyTorch: 2.4.1+cu121
- Datasets: 2.19.2
- 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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