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 directly addresses the question, providing step-by-step instructions
    on how to change the reservation reference from the service page to the booking
    calendar. Each step aligns accurately with the details provided in the document,
    ensuring that the instructions are well-supported and contextually grounded. The
    answer is relevant, concise, and clear, offering precise steps without including
    unnecessary information. It effectively conveys the correct process as described
    in the document.


    Evaluation: Good'
- text: 'Reasoning:

    The answer provided is detailed and outlines the steps to block off time slots
    in the Wix Booking Calendar. However, the question specifically asks about removing
    the time from showing on the booking button, not about blocking off time slots.
    The instructions given do not address the question directly. The document also
    does not mention any method for removing the time from the booking button, so
    the answer lacks context grounding and relevance to both the question and the
    document.


    Evaluation: Bad'
- text: 'Reasoning:

    The provided answer explains steps to enable calendar scheduling and recording
    for the business email, which seems indirectly related but does not address why
    there is no access to these features. The correct approach should elucidate the
    reason behind the lack of access. The document clearly states the issue arises
    due to the domain not being verified and the app not being enabled. Furthermore,
    the answer improperly provides a solution instead of explaining the reason, which
    diverges from the request of why the issue exists. Therefore, it lacks context
    grounding, relevance, and conciseness.


    Evaluation: Bad'
- text: 'Reasoning:

    The answer provided is clear, concise, and directly addresses the question posed.
    It gives a step-by-step guide that is well-grounded in the provided document.
    The document details the necessity of having a premium plan and a connected domain,
    publishing the site, and the exact steps to update the favicon, all of which are
    included in the answer. Additionally, it mentions selecting or uploading an image
    and saving it, which encompasses all key actions needed for the process. The answer
    omits irrelevant details and straightforwardly guides the user according to the
    provided instructions.


    Evaluation: Good'
- text: 'Reasoning:

    The answer provided is well-supported by the document and directly addresses the
    question about making the booking page visible on the site. It concisely explains
    the specific steps needed to add and make the booking page visible, including
    the prerequisite of upgrading to a Business & eCommerce Premium Plan. Each step
    corresponds accurately to the instructions detailed in the provided document,
    ensuring clarity and relevance. The answer also succinctly mentions the availability
    of the service list page and member pages, aligning with the document''s content
    without deviating into unrelated topics.


    Evaluation: Good'
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: 0.5208333333333334
      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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'Reasoning:\nThe answer addresses the question about resolving the issue of <ORGANIZATION> not discovering pages via sitemaps by following the instructions provided in the document. It accurately lists the relevant steps, including checking the sitemap URL path, resubmitting the URL if it was submitted incorrectly, and using the inspection tool to speed up the process. The answer is directly related to the question and avoids unnecessary information. Moreover, it mentions that the "couldn\'t fetch" error is known to occur and can resolve itself, fitting the context provided in the document.\n\nEvaluation: Good'</li><li>'Reasoning:\nThe answer is mostly well-supported by the document and provides a step-by-step process that is directly related to the question about enabling clients to book multiple participants for a service in <ORGANIZATION> Bookings. However, there is a minor but notable error: the instruction to "Enter the John Youngimum number of participants allowed per booking" should correctly say "Enter the maximum number of participants allowed per booking." This typo affects the clarity and correctness of the answer. Additionally, the specific document mentions "Tips" that are omitted in the answer, but this does not significantly detract from the overall utility of the response.\n\nEvaluation: Bad'</li><li>'Reasoning:\nThe answer provided is not supported by the provided document, which does not mention any information related to booking services, changing locations, or known issues with service locations. Therefore, the answer lacks context grounding. Additionally, while it states that a known issue has been resolved, it fails to give clear steps or instructions on what to do if an error is encountered.\n\nEvaluation: Bad'</li></ul> |
| 0     | <ul><li>"Reasoning:\nThe answer accurately reflects the information provided in the document, which states that you cannot transfer or update the booking app on your site. It also correctly includes the suggestion to vote for the desired feature for future updates, which is mentioned in the provided document. The answer is concise, directly addressing the question without unnecessary information, and provides a correct and detailed response based on the document's content.\n\nEvaluation: Good"</li><li>'Reasoning:\nThe answer is directly related to the question and supported by the provided document. It succinctly outlines the steps needed to add a service and sets up service lists on separate pages, aligning with the guidelines in the document. Additionally, it includes instructions on setting up a service page exclusive to site members, which is also supported by the document. The response is clear, detailed, and avoids unnecessary information, providing correct and step-by-step instructions.\n\nEvaluation: Good'</li><li>'Reasoning:\nThe provided answer aligns well with the document\'s content by offering detailed instructions on how to display blog categories using datasets and repeaters or tables. All the steps mentioned (creating datasets, connecting them, adding repeaters or tables, linking datasets, and setting up filters) are grounded in the document. The answer, however, contains placeholder text "95593638" instead of the word "create," which detracts from clarity and correctness. Although minor, the repetition of these placeholders indicates a lack of attention to detail, impacting the conciseness and correctness of the instructions.\n\nEvaluation: Bad'</li></ul>                                                                                  |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.5208   |

## 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_wix_qa_gpt-4o_improved-cot-instructions_chat_few_shot_generated_only_reas")
# Run inference
preds = model("Reasoning:
The answer provided is detailed and outlines the steps to block off time slots in the Wix Booking Calendar. However, the question specifically asks about removing the time from showing on the booking button, not about blocking off time slots. The instructions given do not address the question directly. The document also does not mention any method for removing the time from the booking button, so the answer lacks context grounding and relevance to both the question and the document.

Evaluation: Bad")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 63  | 88.8667 | 151 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 22                    |
| 1     | 23                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0088 | 1    | 0.1951        | -               |
| 0.4425 | 50   | 0.2544        | -               |
| 0.8850 | 100  | 0.152         | -               |
| 1.3274 | 150  | 0.0046        | -               |
| 1.7699 | 200  | 0.0023        | -               |
| 2.2124 | 250  | 0.0019        | -               |
| 2.6549 | 300  | 0.0017        | -               |
| 3.0973 | 350  | 0.0015        | -               |
| 3.5398 | 400  | 0.0014        | -               |
| 3.9823 | 450  | 0.0014        | -               |
| 4.4248 | 500  | 0.0013        | -               |
| 4.8673 | 550  | 0.0013        | -               |

### 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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