---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: avsolatorio/GIST-Embedding-v0
metrics:
- accuracy
widget:
- text: The project is focused on developing a new employee benefits package designed
    to attract and retain top talent. We will conduct competitive benchmarking to
    understand industry standards, gather employee feedback to identify desired benefits,
    and create a comprehensive package that includes health, wellness, and financial
    incentives.
- text: A tire manufacturing company created a new belt to be used as part of tread
    cooling during the manufacturing process. Such a belt is not commercially available.
- text: Covers tasks related to data quality and compliance. This includes handling
    data errors, updating data catalog definitions, and implementing compliance updates.
    The project aims to ensure the accuracy, completeness, and compliance of the company's
    data, thereby increasing its reliability and trustworthiness.
- text: Involves the development, testing, and maintenance of the Huntress agent software.
    This includes fixing bugs, improving error handling, and adding new functionalities.
    The project ensures the agent software is reliable and effective in protecting
    customer systems.
- text: This project involved integrating an off-the-shelf software program into the
    company's existing software infrastructure with the goal of improving the customer
    data allocation and retention processes. The design and development of the integrations
    required to succesfully launch the program within the company's existing software
    architecture required the Python programming language. This development required
    the performance of siginificant testing in an iterative nature by the development
    team because Python had never been used to integrate applications within the company's
    platform previously.
pipeline_tag: text-classification
inference: true
---

# SetFit with avsolatorio/GIST-Embedding-v0

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [avsolatorio/GIST-Embedding-v0](https://huggingface.co/avsolatorio/GIST-Embedding-v0) 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:** [avsolatorio/GIST-Embedding-v0](https://huggingface.co/avsolatorio/GIST-Embedding-v0)
- **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
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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>"A manufacturing corporation undertakes an initiative to restructure its manufacturing organization by designing an organizational structure that will improve the company's business operations"</li><li>"Centers on the production of content for the Brief product. This includes tasks related to drafting insights, creating case studies, and publishing social media posts. The project aims to provide valuable and timely information to Kharon's clients, helping them stay informed about global security topics that impact their commercial activities."</li><li>'The team is developing a comprehensive marketing strategy to increase brand awareness and customer engagement. This includes creating targeted advertising campaigns, optimizing our social media presence, and collaborating with influencers to promote our products. We will also analyze market trends and consumer behavior to refine our approach.'</li></ul>                                                |
| 1     | <ul><li>"Project focused on enhancing the website's functionality, including tasks related to optimizing search functionality and integrating new features such as bookmarks and table of contents for the web reader. The project aims to provide a seamless online experience for customers by improving the efficiency and speed of our website."</li><li>'Design and create an innovative drug delivery system for cancer treatment compatible with different types of cancer and different patient profiles while minimizing negative impacts on healthy tissues'</li><li>'Develop a new and advanced Natural Language Processing (NLP) algorithm to enhance the capabilities of virtual assistants used in various applications, such as customer service chatbots. This project involved improving the NLP algorithm to be more responsive in the area of complex natural language understanding, including context comprehension, sentiment analysis, and accurate response generation'</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("setfit_model_id")
# Run inference
preds = model("A tire manufacturing company created a new belt to be used as part of tread cooling during the manufacturing process. Such a belt is not commercially available.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 23  | 43.5   | 54  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 8                     |
| 1     | 16                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (0.0001, 0.0001)
- head_learning_rate: 0.0001
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0167 | 1    | 0.2764        | -               |
| 0.8333 | 50   | 0.0014        | -               |
| 1.6667 | 100  | 0.0011        | -               |
| 2.5    | 150  | 0.0011        | -               |

### Framework Versions
- Python: 3.9.16
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.1
- Datasets: 2.19.2
- Tokenizers: 0.15.2

## 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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