---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
- accuracy
widget:
- text: mostly works because of the universal themes , earnest performances ... and
    excellent use of music by india 's popular gulzar and jagjit singh .
- text: in all the annals of the movies , few films have been this odd , inexplicable
    and unpleasant .
- text: director charles stone iii applies more detail to the film 's music than to
    the story line ; what 's best about drumline is its energy .
- text: there 's nothing exactly wrong here , but there 's not nearly enough that
    's right .
- text: it 's a bad sign in a thriller when you instantly know whodunit .
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.8621636463481603
      name: Accuracy
---

# SetFit with BAAI/bge-small-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-small-en-v1.5](https://huggingface.co./BAAI/bge-small-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-small-en-v1.5](https://huggingface.co./BAAI/bge-small-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                                                                                                                                                                                                                                                                                                                                                                                                                      |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'a sensitive , modest comic tragedy that works as both character study and symbolic examination of the huge economic changes sweeping modern china .'</li><li>'the year 2002 has conjured up more coming-of-age stories than seem possible , but take care of my cat emerges as the very best of them .'</li><li>'amy and matthew have a bit of a phony relationship , but the film works in spite of it .'</li></ul> |
| 0     | <ul><li>'works on the whodunit level as its larger themes get lost in the murk of its own making'</li><li>"one of those strained caper movies that 's hardly any fun to watch and begins to vaporize from your memory minutes after it ends ."</li><li>"shunji iwai 's all about lily chou chou is a beautifully shot , but ultimately flawed film about growing up in japan ."</li></ul>                                     |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8622   |

## 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("Jorgeutd/setfit-bge-small-v1.5-sst2-50-shot")
# Run inference
preds = model("it 's a bad sign in a thriller when you instantly know whodunit .")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 21.31  | 50  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 50                    |
| 1     | 50                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (10, 10)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0031 | 1    | 0.2515        | -               |
| 0.1567 | 50   | 0.2298        | -               |
| 0.3135 | 100  | 0.2134        | -               |
| 0.4702 | 150  | 0.0153        | -               |
| 0.6270 | 200  | 0.0048        | -               |
| 0.7837 | 250  | 0.0024        | -               |
| 0.9404 | 300  | 0.0023        | -               |
| 1.0972 | 350  | 0.0016        | -               |
| 1.2539 | 400  | 0.0016        | -               |
| 1.4107 | 450  | 0.001         | -               |
| 1.5674 | 500  | 0.0013        | -               |
| 1.7241 | 550  | 0.0008        | -               |
| 1.8809 | 600  | 0.0008        | -               |
| 2.0376 | 650  | 0.0007        | -               |
| 2.1944 | 700  | 0.0008        | -               |
| 2.3511 | 750  | 0.0008        | -               |
| 2.5078 | 800  | 0.0007        | -               |
| 2.6646 | 850  | 0.0006        | -               |
| 2.8213 | 900  | 0.0006        | -               |
| 2.9781 | 950  | 0.0005        | -               |
| 3.1348 | 1000 | 0.0006        | -               |
| 3.2915 | 1050 | 0.0006        | -               |
| 3.4483 | 1100 | 0.0005        | -               |
| 3.6050 | 1150 | 0.0005        | -               |
| 3.7618 | 1200 | 0.0005        | -               |
| 3.9185 | 1250 | 0.0005        | -               |
| 4.0752 | 1300 | 0.0005        | -               |
| 4.2320 | 1350 | 0.0004        | -               |
| 4.3887 | 1400 | 0.0004        | -               |
| 4.5455 | 1450 | 0.0004        | -               |
| 4.7022 | 1500 | 0.0003        | -               |
| 4.8589 | 1550 | 0.0006        | -               |
| 5.0157 | 1600 | 0.0007        | -               |
| 5.1724 | 1650 | 0.0004        | -               |
| 5.3292 | 1700 | 0.0004        | -               |
| 5.4859 | 1750 | 0.0004        | -               |
| 5.6426 | 1800 | 0.0004        | -               |
| 5.7994 | 1850 | 0.0003        | -               |
| 5.9561 | 1900 | 0.0004        | -               |
| 6.1129 | 1950 | 0.0003        | -               |
| 6.2696 | 2000 | 0.0003        | -               |
| 6.4263 | 2050 | 0.0005        | -               |
| 6.5831 | 2100 | 0.0003        | -               |
| 6.7398 | 2150 | 0.0003        | -               |
| 6.8966 | 2200 | 0.0003        | -               |
| 7.0533 | 2250 | 0.0003        | -               |
| 7.2100 | 2300 | 0.0003        | -               |
| 7.3668 | 2350 | 0.0003        | -               |
| 7.5235 | 2400 | 0.0002        | -               |
| 7.6803 | 2450 | 0.0003        | -               |
| 7.8370 | 2500 | 0.0003        | -               |
| 7.9937 | 2550 | 0.0003        | -               |
| 8.1505 | 2600 | 0.0003        | -               |
| 8.3072 | 2650 | 0.0003        | -               |
| 8.4639 | 2700 | 0.0003        | -               |
| 8.6207 | 2750 | 0.0003        | -               |
| 8.7774 | 2800 | 0.0004        | -               |
| 8.9342 | 2850 | 0.0002        | -               |
| 9.0909 | 2900 | 0.0003        | -               |
| 9.2476 | 2950 | 0.0004        | -               |
| 9.4044 | 3000 | 0.0004        | -               |
| 9.5611 | 3050 | 0.0003        | -               |
| 9.7179 | 3100 | 0.0004        | -               |
| 9.8746 | 3150 | 0.0003        | -               |

### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.39.1
- PyTorch: 2.1.0
- Datasets: 2.18.0
- 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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