---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Company C has announced a strategic partnership with Company D, aimed at enhancing
    their technological capabilities.
- text: Two prominent tech companies, DataStream and CloudWorks, have finalized a
    merger that will reshape the industry landscape.
- text: The government has announced new regulations on corporate mergers and acquisitions,
    affecting multiple industries.
- text: PizzaChain has acquired the assets of a struggling rival, resulting in the
    opening of several new outlets quickly.
- text: Company E has expressed intentions to rebrand following a leadership change,
    leaving some wondering about its future acquisitions.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: thenlper/gte-base
model-index:
- name: SetFit with thenlper/gte-base
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.93
      name: Accuracy
---

# SetFit with thenlper/gte-base

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [thenlper/gte-base](https://huggingface.co/thenlper/gte-base) 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:** [thenlper/gte-base](https://huggingface.co/thenlper/gte-base)
- **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                                                                                                                                                                                                                                                                                                                                                                                                                          |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| True  | <ul><li>'Tech Giant A has acquired Startup B in a groundbreaking deal valued at $500 million, aiming to enhance its product offerings.'</li><li>'Industry leaders D and E announced their merger today, combining resources to capture more market share in the competitive landscape.'</li><li>'International Firm I and Domestic Firm J have finalized an acquisition deal that is expected to reshape the industry.'</li></ul> |
| False | <ul><li>'In an unexpected move, Company C has invested heavily in a new technology initiative, signaling its commitment to innovation.'</li><li>'A recent survey indicates that Company F is planning to introduce new features next quarter, boosting user engagement.'</li><li>'Company G has entered a partnership with Organization H, focusing on joint product development and marketing strategies.'</li></ul>             |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.93     |

## 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("amplyfi/merger-and-acquisition")
# Run inference
preds = model("The government has announced new regulations on corporate mergers and acquisitions, affecting multiple industries.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 9   | 14.4496 | 25  |

| Label | Training Sample Count |
|:------|:----------------------|
| False | 243                   |
| True  | 213                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0035 | 1    | 0.1828        | -               |
| 0.1754 | 50   | 0.2934        | -               |
| 0.3509 | 100  | 0.0797        | -               |
| 0.5263 | 150  | 0.0108        | -               |
| 0.7018 | 200  | 0.0013        | -               |
| 0.8772 | 250  | 0.0007        | -               |
| 1.0526 | 300  | 0.0003        | -               |
| 1.2281 | 350  | 0.0002        | -               |
| 1.4035 | 400  | 0.0002        | -               |
| 1.5789 | 450  | 0.0002        | -               |
| 1.7544 | 500  | 0.0002        | -               |
| 1.9298 | 550  | 0.0002        | -               |
| 2.1053 | 600  | 0.0001        | -               |
| 2.2807 | 650  | 0.0001        | -               |
| 2.4561 | 700  | 0.0001        | -               |
| 2.6316 | 750  | 0.0001        | -               |
| 2.8070 | 800  | 0.0001        | -               |
| 2.9825 | 850  | 0.0001        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu124
- Datasets: 3.1.0
- 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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