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 diligently grounds itself with multiple points from the provided document,
    ensuring it is both specific and detailed. It covers key parts of the performance
    review process, including self-assessment, peer feedback, consolidation, coaching
    sessions, and resources. This maintains high relevance and clear grounding in
    the document.

    Evaluation: Good'
- text: 'Reasoning:

    The answer is accurately grounded in the provided document and directly addresses
    the question without deviating into unrelated topics. The email address for contacting
    regarding travel reimbursement questions is correctly cited from the document.


    Final evaluation: Good'
- text: 'Reasoning:

    The answer is adequately grounded in the provided document, covering the steps
    that team leads and employees should take to improve the situation. It includes
    specific actions such as thinking about someone''s time at the company, flagging
    issues, analyzing problems, providing feedback, trying to fix issues together,
    and finishing it up if progress isn''t made. It also mentions asking for upward
    feedback and improving work-life balance, which are directly referenced in the
    documents.


    Final Result: Good'
- text: 'Reasoning:

    The answer is directly related to the document content, addressing the specific
    topic of sexual harassment, which includes flirting making colleagues uncomfortable.
    It covers the importance of creating a respectful environment and mentions the
    consequences of such actions, which is implicitly grounded in the documents''
    information. Thus, it meets the criteria of context grounding, relevance, conciseness,
    and specificity.


    Final Result: Good'
- text: 'Reasoning:

    The answer provides a general rationale for investing in personal relationships
    at work. However, it fails to provide specific points or direct references from
    the documents that explicitly answer the question. Given that the document hints
    at the importance of personal relationships but does not directly explain the
    detailed benefits outlined in the answer, the response is considered somewhat
    speculative and not sufficiently grounded in the provided text.


    Final Result: Bad'
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: 0.5970149253731343
      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 -->
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### 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:\nThe provided answer is too general and is not grounded in the specific details or context of the given document, which focuses on study budgets and considerations for wise spending within the organization.\n\nEvaluation: Bad'</li><li>'Reasoning:\nThe answer contains specific information from the document and lists relevant pet peeves. However, there are multiple instances of the phrase "Cassandra Rivera Heather Nelson," which seems out of place. This could cause confusion and detract from the overall clarity.\n\nFinal Result: Bad'</li><li>"Reasoning:\nThe answer does not address the question directly. The provided steps and methods mentioned focus on handling personal documents, expense reimbursements, secure sharing of information, feedback discussion, and requesting a learning budget. The question specifically asks about accessing the company's training resources, and the answer provided does not stay focused on that.\n\nEvaluation: Bad"</li></ul>                                                                                                                                                                                                         |
| 1     | <ul><li>'Reasoning:\nThe answer accurately addresses the question, providing specific details from the document about how feedback should be given. It includes key points such as timing, focusing on the situation, being clear and direct, showing appreciation, and the intention behind giving feedback, all of which are mentioned in the document.\n\nEvaluation: Good'</li><li>'Reasoning:\nThe answer correctly identifies several reasons why it is important to share information from high-level meetings, directly supported by the provided document. The explanation is clear, relevant, and to the point, avoiding unnecessary information.\n\nEvaluation: Good'</li><li>'Reasoning:\nThe answer largely reproduces content from the document accurately, specifying the process for keeping track of kilometers and sending an email or excel document. However, there are inaccuracies in the email addresses and naming inconsistency. For instance, "Dustin Chan" appears in place of "finance@Dustin Chan.ahouse@example.net" and "ORGANIZATION_2," which do not exist in the document. Moreover, it includes extraneous, non-verifiable information about the parking card.\n\nEvaluation:\nBad'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.5970   |

## 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_newrelic_gpt-4o_improved-cot-instructions_chat_few_shot_only_reasoning_17")
# Run inference
preds = model("Reasoning:
The answer is accurately grounded in the provided document and directly addresses the question without deviating into unrelated topics. The email address for contacting regarding travel reimbursement questions is correctly cited from the document.

Final evaluation: Good")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 17  | 49.2462 | 126 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 32                    |
| 1     | 33                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (5, 5)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0061 | 1    | 0.237         | -               |
| 0.3067 | 50   | 0.2667        | -               |
| 0.6135 | 100  | 0.2263        | -               |
| 0.9202 | 150  | 0.0511        | -               |
| 1.2270 | 200  | 0.004         | -               |
| 1.5337 | 250  | 0.0024        | -               |
| 1.8405 | 300  | 0.0019        | -               |
| 2.1472 | 350  | 0.0019        | -               |
| 2.4540 | 400  | 0.0017        | -               |
| 2.7607 | 450  | 0.0015        | -               |
| 3.0675 | 500  | 0.0014        | -               |
| 3.3742 | 550  | 0.0014        | -               |
| 3.6810 | 600  | 0.0013        | -               |
| 3.9877 | 650  | 0.0013        | -               |
| 4.2945 | 700  | 0.0013        | -               |
| 4.6012 | 750  | 0.0013        | -               |
| 4.9080 | 800  | 0.0012        | -               |

### 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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