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 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 accurately addresses the question on choosing toys for a rabbit
    by referencing relevant information included in the document. It highlights the
    types of suitable toys made of different materials, emphasizes safety and non-toxicity,
    and presents a variety of toy options. The key guidelines from the document are
    reflected properly, ensuring the advice is appropriate and useful.


    Evaluation:'
- text: 'Reasoning:

    irrelevant - The answer provided is addressing a different subject than what is
    being asked. The question asks about Aaron Pryor’s manager during his boxing career,
    while the answer talks about Kieron Freeman''s football career and his loan spells,
    which is completely unrelated.


    Evaluation:'
- text: 'Reasoning:

    confabulation - The answer contains information not at all located in the document
    or subverts the essence/context of the provided text.

    Evaluation:'
- text: 'Reasoning:

    hallucination - The answer includes procedures such as teaching the dog the "fly"
    command and using a magic spell, which are not mentioned inthe document and are
    impractical.

    Evaluation:'
- text: 'Reasoning:

    The answer correctly identifies that Allan Cox''s First Class Delivery was launched
    on a H128-10W for his Level 1 certification flight, which is directly supported
    by the document.


    Evaluation:'
inference: true
---

# 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:\nfactual error - The answer mentions that film under-exposes better and that the author finds 5MP digital sensors comparable to high-end film, both of which are not supported by the provided document. The document specifically discusses how film over-exposes better and that 10MP digital sensors are comparable to common, non-professional film.\n\nEvaluation:'</li><li>'Reasoning:\nirrelevant - The answer provided is not relevant to the question being asked. The document contains information about martial arts event results and no information about the Arcana Chronicles or its main conflicts.\n\nEvaluation:'</li></ul>                                                                                         |
| 1     | <ul><li>"Reasoning:\nThe answer effectively covers key best practices from the document that a web designer can incorporate into their client discovery and web design process to avoid unnecessary revisions and conflicts. These best practices include getting to know the client's needs, signing a detailed contract, showcasing honesty and diplomacy, and effective communication.\n\nEvaluation:"</li><li>"Reasoning:\nCorrect extraction from the document - The answer provided accurately reflects the author's views as expressed in the document. The focus on drawing from one's experiences, especially those involving pain and emotion, to create a genuine and relatable story aligns with the author's beliefs.\n\nEvaluation:"</li><li>'Reasoning:\nThe answer correctly identifies Mauro Rubin as the CEO of JoinPad, which is explicitly mentioned in the document. \nEvaluation:'</li></ul> |

## 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_gpt-4o_improved-cot_chat_few_shot_remove_final_evaluation_e1_one_big_mode")
# Run inference
preds = model("Reasoning:
confabulation - The answer contains information not at all located in the document or subverts the essence/context of the provided text.
Evaluation:")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 30.7647 | 156 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 200                   |
| 1     | 208                   |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0010 | 1    | 0.1939        | -               |
| 0.0490 | 50   | 0.2385        | -               |
| 0.0980 | 100  | 0.1772        | -               |
| 0.1471 | 150  | 0.1461        | -               |
| 0.1961 | 200  | 0.0922        | -               |
| 0.2451 | 250  | 0.1027        | -               |
| 0.2941 | 300  | 0.078         | -               |
| 0.3431 | 350  | 0.0695        | -               |
| 0.3922 | 400  | 0.0617        | -               |
| 0.4412 | 450  | 0.0389        | -               |
| 0.4902 | 500  | 0.0358        | -               |
| 0.5392 | 550  | 0.0355        | -               |
| 0.5882 | 600  | 0.0299        | -               |
| 0.6373 | 650  | 0.0318        | -               |
| 0.6863 | 700  | 0.0286        | -               |
| 0.7353 | 750  | 0.0323        | -               |
| 0.7843 | 800  | 0.0228        | -               |
| 0.8333 | 850  | 0.0284        | -               |
| 0.8824 | 900  | 0.0252        | -               |
| 0.9314 | 950  | 0.0342        | -               |
| 0.9804 | 1000 | 0.0251        | -               |

### 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->