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 correctly identifies the year 1842, aligning directly with the details
    provided in the document, addressing the specific question asked without any deviation.


    Evaluation:'
- text: 'Reasoning:

    Correct- the answer correctly cites that the average student travels more than
    750 miles to study at Notre Dame, as found in the document.

    Evaluation:'
- text: 'Reasoning:

    Everything stated in the answer is directly supported by the document and is relevant
    to the question asked. The answer concisely provides the specific information
    required without deviating into unnecessary details.


    Evaluation:'
- text: 'Reasoning:

    contradiction - The answer includes incorrect information regarding Karl Marx
    that is not supported by the document and is not relevant to the question. The
    correct aspect is that "The Review of Politics was inspired by German Catholic
    journals."


    Evaluation:'
- text: 'Reasoning:

    The answer is correctly grounded in the provided document, which specifies that
    Forbes.com ranked Notre Dame 8th among research universities. It is also relevant
    to the specific question asked, and the response is clear and concise without
    additional unnecessary information.


    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: 0.8983050847457628
      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:\ncontext grounded - The answer correctly includes Joan Gaspart's presidency resignation due to the team's poor performance in the 2003 season, whichis supported by the document.\nEvaluation:"</li><li>'Reasoning:\nwrong name - The name "Father Josh Carrier" does not appear in the document; the correct name is "Father Joseph Carrier."\nEvaluation:'</li><li>"Reasoning:\nhallucination - The answer is incorrect, and it's contradicted.\nEvaluation:"</li></ul> |
| 0     | <ul><li>'Reasoning:\nhallucination - The answer contains information that contradicts what appears in the document.\nEvaluation:'</li><li>'Reasoning:\nirrelevant - The answeris not relevant to what is asked.\nEvaluation:'</li><li>'Reasoning:\nContradiction - The answer states Manhattan, but the document clearly indicates that Queens is the borough with the highest population of Asian-Americans.\n\nEvaluation:'</li></ul>                                                       |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8983   |

## 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_squad_gpt-4o_improved-cot-instructions_chat_few_shot_remove_final_evaluat")
# Run inference
preds = model("Reasoning:
Correct- the answer correctly cites that the average student travels more than 750 miles to study at Notre Dame, as found in the document.
Evaluation:")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 34.4637 | 148 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 79                    |
| 1     | 100                   |

### 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    | 0.2446        | -               |
| 0.1116 | 50   | 0.2299        | -               |
| 0.2232 | 100  | 0.1175        | -               |
| 0.3348 | 150  | 0.0861        | -               |
| 0.4464 | 200  | 0.0436        | -               |
| 0.5580 | 250  | 0.0235        | -               |
| 0.6696 | 300  | 0.0262        | -               |
| 0.7812 | 350  | 0.0146        | -               |
| 0.8929 | 400  | 0.015         | -               |

### 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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