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:


    **Context Grounding:**

    - The answer accurately pulls information directly from the provided document,
    including specific changes Haribabu Kommi is making to the storage AM. It lists
    the changes in a manner that seems consistent with the details given in the document.


    **Relevance:**

    - The answer is directly relevant to the question, which asked specifically about
    the changes Haribabu Kommi is making to the storage AM. It enumerates the exact
    modifications and additions that are being incorporated based on the email content.


    **Conciseness:**

    - The answer is concise and to the point, listing only the changes mentioned in
    the supplied email without deviating into unrelated topics or providing extraneous
    information.


    Final Result: Good'
- text: '**Good**


    **Reasoning:**

    1. **Context Grounding:** The answer "China''s Ning Zhongyan won the gold medal
    in the men''s 1,500m final at the speed skating World Cup" is well-supported by
    the provided document, which explicitly states that Ning Zhongyan won the gold
    medal in the men''s 1,500m final.

    2. **Relevance:** The answer directly addresses the specific question asked, identifying
    the athlete who won the gold medal in the men''s 1,500m final.

    3. **Conciseness:** The answer is clear and to the point, providing only the necessary
    information without any additional, unrelated details.'
- text: 'Reasoning why the answer may be good:

    1. **Context Grounding:** The details in the answer about the sizes of the individual
    and combined portraits are directly pulled from the provided document.

    2. **Relevance:** The answer strictly addresses the question about the available
    sizes for the individual and combined portraits without deviating into unrelated
    topics.

    3. **Conciseness:** The answer is concise, directly providing the requested size
    information without including extraneous details.


    Reasoning why the answer may be bad:

    1. There is no discernible reason why this answer may be bad based on the provided
    criteria. It is well-supported by the document, directly answers the question,
    and is concise.


    Final Result: **Good**'
- text: 'Reasoning why the answer may be good:

    1. **Context Grounding:** The answer accurately lists the components found in
    the provided document, such as comprehension questions, writing exercises, discussion
    questions, an additional reading list, semester and full-year schedules, and a
    bibliography. It also includes details about the organization of the guide into
    units and lessons, which is mentioned in the document.

    2. **Relevance:** The answer specifically addresses the question by identifying
    the components of the British Medieval Student Guide.

    3. **Conciseness:** The answer is relatively to the point, mentioning the main
    components without unnecessary elaboration.


    Reasoning why the answer may be bad:

    1. **Context Grounding:** Although the details are generally correct, some parts
    of the provided description are omitted, such as the note that comprehension question
    answers are in the Teacher''s Guide.

    2. **Relevance:** The initial part about the introductory question "Why read great
    literature?" and some other additional comments are not directly related to the
    components of the Student Guide.

    3. **Conciseness:** The answer could be more concise by excluding repeated and
    unrelated information, focusing only on listing the components directly.


    Final Result: **Bad**


    The answer introduces unnecessary elements that are not related to enumerating
    the components of the guide, and it overlooks some specific details provided in
    the document. Overall, the response is correct but not optimal in addressing the
    specific question concisely.'
- text: '**Reasoning:**


    **Why the answer may be good:**

    1. It lists three names of Members of Congress, which directly responds to the
    question.


    **Why the answer may be bad:**

    1. **Context Grounding:** The provided document specifically names Rep. Danny
    Davis as the third Member of Congress, and the first two were Reps. Keith Ellison
    and Barbara Lee, not Andy Harris, Kyle Evans, or Jessica Smith. This indicates
    that the actual names provided in the answer are incorrect and not grounded in
    the given context.

    2. **Relevance:** The answer is irrelevant because it provides incorrect names,
    which does not address the question accurately.

    3. **Conciseness:** The answer is concise, but since it’s incorrect, its brevity
    doesn''t contribute to its correctness.


    **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: 0.92
      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:**\n\n**Why the Answer May Be Good:**\n1. **Context Grounding:** The answer references the points made in the document, such as Coach Brian Shaw's strategy of pushing the ball after makes and misses as well as encouraging players to take the first available shot within the rhythm of the offense.\n2. **Relevance:** The answer directly addresses why the Nuggets are having an offensive outburst, highlighting the coaching strategy and players' adaptation.\n3. **Conciseness:** The answer is mostly to the point and focuses on the main question.\n\n**Why the Answer May Be Bad:**\n1. **Context Grounding:** The mention of a new training technique using virtual reality is not supported by any information within the document provided.\n2. **Conciseness:** The additional detail about the virtual reality training is unnecessary given that it is not referenced in the document and does not contribute to answering the specific question about the offensive outburst.\n   \n**Final Result:**\nBased on the evaluation criteria, the inclusion of fictitious or unsupported information about the virtual reality training significantly detracts from the answer’s credibility and relevance.\n\n**Bad**"</li><li>'Reasoning why the answer may be good:\n1. **Context Grounding:** The provided answer cites specific information about film and digital photography directly from the provided document, showing a good grounding.\n2. **Relevance:** The answer addresses the specific question by discussing different aspects such as exposure tolerance, color capture, and overall image resolution between film and digital photography.\n3. **Conciseness:** The answer is relatively concise and sticks to the main points relevant to the question without unnecessary elaboration.\n\nReasoning why the answer may be bad:\n1. **Overly Detailed:** The answer could be seen as too detailed in certain segments, which might slightly detract from conciseness.\n2. **Possible Confusion:** The mention of specific technical details like "5MP digital sensors" could confuse readers who are not familiar with the technical specifications, detracting from clarity.\n3. **Omission of Key Comparison Points:** The answer does not touch upon some of the more subjective observations made by the author, like the practical advantages in using film for certain types of photography.\n\nFinal Result: Good'</li><li>'Reasoning:\n1. **Context Grounding**: The answer provided does not reference the third book of the Arcana Chronicles by Kresley Cole or even discuss any content relevant to it. Instead, it discusses an MMA event in Calgary, Alberta, Canada.\n2. **Relevance**: The answer is entirely irrelevant to the question. The question is about the main conflict in the third book of a specific book series, but the answer describes an MMA fight event.\n3. **Conciseness**: While the answer is concise in its context, it is entirely off-topic and therefore does not satisfy the conciseness criterion in a meaningful way.\n\nThe answer may be deemed bad because it does not address the question about the Arcana Chronicles at all and instead provides unrelated information about an MMA event.\n\nFinal result: Bad'</li></ul> |
| 1     | <ul><li>'Reasoning:\n\n1. Context Grounding:\n   - Good: The answer is supported by the document. The suggestions mentioned (getting to know the client, signing a contract, and showcasing honesty and diplomacy) are directly referenced in the text provided.\n   - Bad: There is no significant bad aspect in terms of context grounding; the answer sticks closely to the source material.\n\n2. Relevance:\n   - Good: The answer is highly relevant to the question about best practices to avoid unnecessary revisions and conflicts. It addresses client understanding, contractual agreements, and the handling of extra charges—all crucial for minimizing conflicts.\n   - Bad: There is no deviation from the topic. The answer is focused solely on the best practices, as asked in the question.\n\n3. Conciseness:\n   - Good: The answer is concise and to the point, effectively summarizing the practices without unnecessary details.\n   - Bad: The level of detail might be too succinct for some readers looking for more in-depth discussion, but this is minor given the criteria.\n\nFinal Result:\nGood'</li><li>"Reasoning for why the answer may be good:\n- The answer references the author’s emphasis on drawing from personal experiences of pain and emotion to create genuine and relatable characters, which is well-supported by the document.\n- It highlights the importance of genuineness and relatability, which aligns directly with the content provided in the document.\n- The answer stays focused on the specific question about creating a connection between the reader and the characters.\n\nReasoning for why the answer may be bad:\n- The answer could be seen as slightly verbose and might include more detail than necessary, rather than being extremely concise.\n- It does not explicitly mention the document's use of pain for romance authors specifically, which might add to the context.\n\nFinal result: Good"</li><li>"**Reasoning:**\n\n**Pros:**\n1. **Context Grounding:** The document explicitly states that Mauro Rubin is the CEO of JoinPad and mentions that he was speaking at the event, which directly supports the answer.\n2. **Relevance:** The answer directly and correctly responds to the question about the CEO's identity during the event.\n3. **Conciseness:** The answer is brief and to the point, providing only the necessary information.\n\n**Cons:**\n- There are no significant cons as the answer fulfills all criteria effectively.\n\n**Final Result:** Good"</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.92     |

## 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_two_reasoning_only_reasoning_1726")
# Run inference
preds = model("**Good**

**Reasoning:**
1. **Context Grounding:** The answer \"China's Ning Zhongyan won the gold medal in the men's 1,500m final at the speed skating World Cup\" is well-supported by the provided document, which explicitly states that Ning Zhongyan won the gold medal in the men's 1,500m final.
2. **Relevance:** The answer directly addresses the specific question asked, identifying the athlete who won the gold medal in the men's 1,500m final.
3. **Conciseness:** The answer is clear and to the point, providing only the necessary information without any additional, unrelated details.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median   | Max |
|:-------------|:----|:---------|:----|
| Word count   | 52  | 125.5070 | 199 |

| 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    | 0.2031        | -               |
| 0.2809 | 50   | 0.2589        | -               |
| 0.5618 | 100  | 0.2125        | -               |
| 0.8427 | 150  | 0.0079        | -               |
| 1.1236 | 200  | 0.0022        | -               |
| 1.4045 | 250  | 0.0017        | -               |
| 1.6854 | 300  | 0.0017        | -               |
| 1.9663 | 350  | 0.0014        | -               |
| 2.2472 | 400  | 0.0014        | -               |
| 2.5281 | 450  | 0.0012        | -               |
| 2.8090 | 500  | 0.0012        | -               |
| 3.0899 | 550  | 0.0012        | -               |
| 3.3708 | 600  | 0.0012        | -               |
| 3.6517 | 650  | 0.0011        | -               |
| 3.9326 | 700  | 0.0011        | -               |
| 4.2135 | 750  | 0.0011        | -               |
| 4.4944 | 800  | 0.0011        | -               |
| 4.7753 | 850  | 0.001         | -               |

### 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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