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:\nThe provided answer is accurate and directly addresses the question\
    \ by listing the specific changes Haribabu Kommi is making to the storage AM as\
    \ detailed in his email. The changes include support for the storage method in\
    \ the `create access method` syntax, adding necessary storage AM API hooks, updating\
    \ the relation structure with a storageAm routine pointer, moving tuple visibility\
    \ functions to heap storage AM, adding slot-specific storage AM routine pointers,\
    \ implementing write support functionality, and adding all scan-supported functions\
    \ to storage AM. Additionally, the potential need for further modifications due\
    \ to assumptions about the tuple format is mentioned. \n\nThe answer remains relevant\
    \ and concise, strictly sticking to the context given in the document.\n\nFinal\
    \ Evaluation: Good"
- text: 'Reasoning:

    The answer directly addresses the question by stating that China''s Ning Zhongyan
    won the gold medal in the men''s 1,500m final at the speed skating World Cup.
    This information is clearly found in the document, which confirms Ning''s achievement
    at the event in Stavanger, Norway. The answer is concise, relevant, and well-supported
    by the given context, avoiding extraneous details.


    Final Evaluation: Good'
- text: 'Reasoning:

    The provided answer directly addresses the specific question regarding the available
    sizes for the individual and combined portraits of David Bowie by Ian Davies.
    The sizes mentioned (30 x 28.5 inch and 40 x 38.5 inch for individual portraits,
    and 60 inch x 30 inch and 80 inch x 40 inch for combined portraits) are accurate
    and well-supported by the document. The answer remains relevant, concise, and
    fully grounded in the context provided in the document without deviating into
    unnecessary details.


    Final Evaluation: Good

    Evaluation: Good'
- text: 'Reasoning:

    The answer provided is accurate and focuses on the components of the British Medieval
    Student Guide as described in the document. It mentions comprehension questions,
    writing exercises, discussion questions, an additional reading list, semester
    and full-year schedules, and a bibliography. It also correctly identifies the
    organization of the material into 4 units with 2 lessons each, including Introduction,
    Comprehension Questions, Literary Lesson, and Writing Exercises, along with an
    Appendix Section. These points are directly supported by the information in the
    provided document.


    Final Evaluation: good

    Evaluation: Good'
- text: 'Reasoning:

    The answer incorrectly names Rep. Andy Harris, Reps. Kyle Evans, and Jessica Smith
    as the first three Members of Congress calling for an end to the blockade of Gaza.
    The document clearly states that the first three Members of Congress to issue
    such public statements were Rep. Keith Ellison, Rep. Barbara Lee, and Rep. Danny
    Davis. The answer does not align with the provided information, indicating a lack
    of context grounding and relevance to the specific factual content required by
    the question.


    Final Evaluation: 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: 0.9333333333333333
      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:\nThe answer provides key insights into the reasons behind the Denver Nuggets' offensive outburst in January, specifically mentioning the comfort and effectiveness of the team, the coaching strategy of taking the first available shot in the rhythm of the offense, and emphasizing pushing the ball after both makes and misses. These points are directly supported by the document. However, the inclusion of information about a new training technique involving virtual reality is not supported by the provided document and thus detracts from the answer's accuracy and relevance.\n\nFinal Evaluation: Bad"</li><li>'Reasoning:\nWhile the provided answer attempts to address the differences between film and digital photography, it contains several inaccuracies and inconsistencies with the document. The answer incorrectly states that film under-exposes better and compresses the range into the bottom end, whereas the document clearly states that film over-exposes better and compresses the range into the top end. Additionally, it mentions that the digital sensors capture all three colors at each point, which is inaccurate as per the document; it states digital sensors capture only one color at each point and then interpolate the data to create an RGB image. Moreover, the answer states the comparison is to 5MP digital sensors whereas the document talks about 10MP sensors.\n\nThese inaccuracies undermine the grounding and reliability of the answer.\n\nFinal Evaluation: Bad'</li><li>"Reasoning:\nThe provided answer addresses a topic entirely unrelated to the given question. The question is about the main conflict in the third book of the Arcana Chronicles by Kresley Cole, but the answer discusses the result of an MMA event featuring Antonio Rogerio Nogueira. There is no connection between the document's content and the question, leading to a clear lack of context grounding, relevance, and conciseness.\n\nFinal Evaluation: Bad \nEvaluation: Bad"</li></ul> |
| 1     | <ul><li>'Reasoning:\nThe answer provided effectively draws upon the document to list several best practices that a web designer can incorporate into their client discovery and web design process to avoid unnecessary revisions and conflicts. Each practice mentioned—getting to know the client and their needs, signing a detailed contract, and communicating honestly about extra charges—is directly supported by points raised in the document. The answer is relevant, concise, and closely aligned with the specific question asked.\n\nFinal Evaluation: Good'</li><li>"Reasoning:\nThe answer provided correctly identifies the author's belief on what creates a connection between the reader and the characters in a story. It states that drawing from the author's own painful and emotional experiences makes the story genuine and relatable, thus engaging the reader. This is consistent with the document, which emphasizes the importance of authenticity and emotional depth derived from the author's personal experiences to make characters and their struggles real to the reader. The answer is relevant to the question and concisely captures the essence of the author's argument without including unnecessary information.\n\nFinal Evaluation: Good\n"</li><li>'Reasoning:\nThe answer correctly identifies Mauro Rubin as the CEO of JoinPad during the event at Talent Garden Calabiana, Milan. This is well-supported by the context provided in the document, which specifically mentions Mauro Rubin, the JoinPad CEO, taking the stage at the event. The answer is relevant to the question and concise.\n\nFinal Evaluation: Good\nEvaluation: Good'</li></ul>                                                                                                                                                                                                                                                                                                                                                          |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9333   |

## 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_rag_ds_gpt-4o_improved-cot-instructions_chat_few_shot_generated_only_reas")
# Run inference
preds = model("Reasoning:
The answer directly addresses the question by stating that China's Ning Zhongyan won the gold medal in the men's 1,500m final at the speed skating World Cup. This information is clearly found in the document, which confirms Ning's achievement at the event in Stavanger, Norway. The answer is concise, relevant, and well-supported by the given context, avoiding extraneous details.

Final Evaluation: Good")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 34  | 75.1972 | 127 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 34                    |
| 1     | 37                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0056 | 1    | 0.2224        | -               |
| 0.2809 | 50   | 0.2466        | -               |
| 0.5618 | 100  | 0.1203        | -               |
| 0.8427 | 150  | 0.0068        | -               |
| 1.1236 | 200  | 0.0029        | -               |
| 1.4045 | 250  | 0.0021        | -               |
| 1.6854 | 300  | 0.002         | -               |
| 1.9663 | 350  | 0.0017        | -               |
| 2.2472 | 400  | 0.0017        | -               |
| 2.5281 | 450  | 0.0015        | -               |
| 2.8090 | 500  | 0.0014        | -               |
| 3.0899 | 550  | 0.0013        | -               |
| 3.3708 | 600  | 0.0014        | -               |
| 3.6517 | 650  | 0.0011        | -               |
| 3.9326 | 700  | 0.0012        | -               |
| 4.2135 | 750  | 0.0012        | -               |
| 4.4944 | 800  | 0.0012        | -               |
| 4.7753 | 850  | 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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