
# 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 system shall allow user to confirm the completion of current configuration'</li><li>'The user will be able to end the game at any stage of the game. The user will then be able to quit the program or begin/join a new game.'</li><li>'This may alternately be done through a command-line interface.'</li></ul>                          |
| 0     | <ul><li>'The properties that the GCS sends to the GCSMiddleware shall be defined in JSON'</li><li>'Host OS initiates process, puts editor in process, sends the filename as argument.'</li><li>'− Project testers can use this document as a base for their testing strategy as some bugs are easier to find using a requirements document. '</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy | Weighted Precision | Weighted Recall | Weighted F1 | Macro Precision | Macro Recall | Macro F1 |
|:--------|:---------|:-------------------|:----------------|:------------|:----------------|:-------------|:---------|
| **all** | 0.7414   | 0.7699             | 0.7414          | 0.7380      | 0.7638          | 0.7500       | 0.7394   |

## 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 Clarus system shall acquire, process, and disseminate surface data.")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 5   | 22.8333 | 116 |

| 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    | 0.411         | -               |
| 0.3333 | 50   | 0.2171        | -               |
| 0.6667 | 100  | 0.0591        | -               |
| 1.0    | 150  | 0.0006        | -               |
| 1.3333 | 200  | 0.0001        | -               |
| 1.6667 | 250  | 0.0001        | -               |
| 2.0    | 300  | 0.0           | -               |
| 2.3333 | 350  | 0.0001        | -               |
| 2.6667 | 400  | 0.0001        | -               |
| 3.0    | 450  | 0.0           | -               |
| 3.3333 | 500  | 0.0001        | -               |
| 3.6667 | 550  | 0.0001        | -               |
| 4.0    | 600  | 0.0           | -               |
| 4.3333 | 650  | 0.0           | -               |
| 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  | 0.0           | -               |
| 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: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.15.0
- Tokenizers: 0.15.0

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
{"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": "In step one the GCS announces itself to the GCSMiddleware providing its unique ID"}, {"text": "Ratio of the total number of hours during a certain period, exclud- ing the number of hours that the wind turbine could not be operated due to maintenance or fault situations, to the total number of hours in the period, expressed as a percentage. [1]"}, {"text": "The Clarus system shall acquire, process, and disseminate surface data."}, {"text": "Alternatives to a particular step are suggested by the flag \u201cALT <n>\u201d where <n> is an alternative listed below the scenario. "}, {"text": "The MissionPlanner shall provide an interface to support the execution of predefined missions specified in JSON"}], "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": 0.7413554633471646, "name": "Accuracy"}, {"type": "weighted precision", "value": 0.769921403401218, "name": "Weighted Precision"}, {"type": "weighted recall", "value": 0.7413554633471646, "name": "Weighted Recall"}, {"type": "weighted f1", "value": 0.7379592824627539, "name": "Weighted F1"}, {"type": "macro precision", "value": 0.7638282097658351, "name": "Macro Precision"}, {"type": "macro recall", "value": 0.7500176490334813, "name": "Macro Recall"}, {"type": "macro f1", "value": 0.7394081119226172, "name": "Macro F1"}]}]}]}
setfit safetensors roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/all-roberta-large-v1 model-index region:us