README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Zouk Capital invests £35 million into Energy Park through CIIF financing
- text: Volkswagen Sets Ambitious Goals for Electric Vehicle Production
- text: LATAM Unveils New Dreamliner Economy Cabin Design
- text: Emirates Announces Additional Flights for Eid Al Fitr
- text: Japan Airlines Unveils ‘MYAKU-MYAKU’ Dreamliner Livery
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: thenlper/gte-small
model-index:
- name: SetFit with thenlper/gte-small
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.4864864864864865
      name: Accuracy
---

# SetFit with thenlper/gte-small

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) as the Sentence Transformer embedding model. A OneVsRestClassifier 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-small](https://huggingface.co/thenlper/gte-small)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **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)

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.4865   |

## 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/gte-small_all-labels_multilabel")
# Run inference
preds = model("LATAM Unveils New Dreamliner Economy Cabin Design")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 9.9616 | 30  |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0018 | 1    | 0.3005        | -               |
| 0.0903 | 50   | 0.2933        | -               |
| 0.1805 | 100  | 0.2219        | -               |
| 0.2708 | 150  | 0.1568        | -               |
| 0.3610 | 200  | 0.1334        | -               |
| 0.4513 | 250  | 0.1204        | -               |
| 0.5415 | 300  | 0.1215        | -               |
| 0.6318 | 350  | 0.1154        | -               |
| 0.7220 | 400  | 0.1065        | -               |
| 0.8123 | 450  | 0.0935        | -               |
| 0.9025 | 500  | 0.0892        | -               |
| 0.9928 | 550  | 0.0807        | -               |
| 1.0830 | 600  | 0.0776        | -               |
| 1.1733 | 650  | 0.0716        | -               |
| 1.2635 | 700  | 0.06          | -               |
| 1.3538 | 750  | 0.0677        | -               |
| 1.4440 | 800  | 0.0607        | -               |
| 1.5343 | 850  | 0.065         | -               |
| 1.6245 | 900  | 0.0593        | -               |
| 1.7148 | 950  | 0.0622        | -               |
| 1.8051 | 1000 | 0.064         | -               |
| 1.8953 | 1050 | 0.0624        | -               |
| 1.9856 | 1100 | 0.0667        | -               |

### 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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