README.md

---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- weighted precision
- weighted recall
- weighted f1
- macro precision
- macro recall
- macro f1
widget:
- text: Roles can be assigned to a user account for individual products.
- text: The number of active Subscription Versions in a sample to be monitored by
    the NPAC SMS.
- text: 'The visual representation of an SDT or a part of an SDT. '
- text: Open Society Institute Guide to Institutional Repository Software, 3rd ed.
    (2004)
- text: 'The Application/Delete menu item shall provide an interface for deleting
    an application and all the files in the application directory. '
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-roberta-large-v1
model-index:
- name: SetFit with sentence-transformers/all-roberta-large-v1
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.7621000820344545
      name: Accuracy
    - type: weighted precision
      value: 0.7627752679232598
      name: Weighted Precision
    - type: weighted recall
      value: 0.7621000820344545
      name: Weighted Recall
    - type: weighted f1
      value: 0.7621663772102192
      name: Weighted F1
    - type: macro precision
      value: 0.7621734718049769
      name: Macro Precision
    - type: macro recall
      value: 0.7624659767698817
      name: Macro Recall
    - type: macro f1
      value: 0.7620481988534211
      name: Macro F1
---

# SetFit with sentence-transformers/all-roberta-large-v1

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) 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:** [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 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>'The matrix dimensions are fixed, and are the same when displaying departments or categories.'</li><li>'The Clarus program shall provide for customer service.'</li><li>'NPAC SMS shall identify the originator of any accessible system resources.'</li></ul>                                                                                                                                                                                                                                                                                                                  |
| 0     | <ul><li>'A search pattern is a string w such that w is a sub-string of a string α and α is a string derived from some non- terminal β in the target grammar.'</li><li>'Normally only one or two parties are engaged in operation and maintenance of the wind turbine(s), typically the owner and the operation and maintenance organisation, which in some cases is one and the same.'</li><li>'TASE-2 (ICCP) resides on layer 7 in the OSI-model and is an MMS companion standard, that is, the general MMS services have been particularised for telecontrol applications.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy | Weighted Precision | Weighted Recall | Weighted F1 | Macro Precision | Macro Recall | Macro F1 |
|:--------|:---------|:-------------------|:----------------|:------------|:----------------|:-------------|:---------|
| **all** | 0.7621   | 0.7628             | 0.7621          | 0.7622      | 0.7622          | 0.7625       | 0.7620   |

## 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("kwang123/roberta-large-setfit-ReqORNot")
# Run inference
preds = model("The visual representation of an SDT or a part of an SDT. ")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 5   | 21.7708 | 46  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 24                    |
| 1     | 24                    |

### Training Hyperparameters
- batch_size: (8, 8)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0067 | 1    | 0.3795        | -               |
| 0.3333 | 50   | 0.298         | -               |
| 0.6667 | 100  | 0.0025        | -               |
| 1.0    | 150  | 0.0002        | -               |
| 1.3333 | 200  | 0.0002        | -               |
| 1.6667 | 250  | 0.0001        | -               |
| 2.0    | 300  | 0.0001        | -               |
| 2.3333 | 350  | 0.0001        | -               |
| 2.6667 | 400  | 0.0001        | -               |
| 3.0    | 450  | 0.0001        | -               |
| 3.3333 | 500  | 0.0           | -               |
| 3.6667 | 550  | 0.0           | -               |
| 4.0    | 600  | 0.0           | -               |
| 4.3333 | 650  | 0.0001        | -               |
| 4.6667 | 700  | 0.0           | -               |
| 5.0    | 750  | 0.0           | -               |
| 5.3333 | 800  | 0.0           | -               |
| 5.6667 | 850  | 0.0           | -               |
| 6.0    | 900  | 0.0           | -               |
| 6.3333 | 950  | 0.0001        | -               |
| 6.6667 | 1000 | 0.0           | -               |
| 7.0    | 1050 | 0.0           | -               |
| 7.3333 | 1100 | 0.0           | -               |
| 7.6667 | 1150 | 0.0           | -               |
| 8.0    | 1200 | 0.0           | -               |
| 8.3333 | 1250 | 0.0           | -               |
| 8.6667 | 1300 | 0.0           | -               |
| 9.0    | 1350 | 0.0           | -               |
| 9.3333 | 1400 | 0.0           | -               |
| 9.6667 | 1450 | 0.0           | -               |
| 10.0   | 1500 | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- Transformers: 4.38.1
- PyTorch: 2.1.0+cu121
- 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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