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--- |
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library_name: setfit |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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metrics: |
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- accuracy |
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widget: |
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- text: we get some truly unique character studies and a cross-section of americana |
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that hollywood could n't possibly fictionalize and be believed . |
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- text: the movie is one of the best examples of artful large format filmmaking you |
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are likely to see anytime soon . |
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- text: my response to the film is best described as lukewarm . |
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- text: the movie 's ripe , enrapturing beauty will tempt those willing to probe its |
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inscrutable mysteries . |
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- text: fear dot com is so rambling and disconnected it never builds any suspense |
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. |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.5380090497737556 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) 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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 5 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 0 | <ul><li>"it 's not a motion picture ; it 's an utterly static picture ."</li><li>"frankly , it 's kind of insulting , both to men and women ."</li><li>'under-rehearsed and lifeless'</li></ul> | |
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| 2 | <ul><li>"recoing 's fantastic performance does n't exactly reveal what makes vincent tick , but perhaps any definitive explanation for it would have felt like a cheat ."</li><li>"do n't expect any subtlety from this latest entry in the increasingly threadbare gross-out comedy cycle ."</li><li>"merry friggin ' christmas !"</li></ul> | |
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| 3 | <ul><li>"so purely enjoyable that you might not even notice it 's a fairly straightforward remake of hollywood comedies such as father of the bride ."</li><li>"what saves this deeply affecting film from being merely a collection of wrenching cases is corcuera 's attention to detail ."</li><li>'for once , a movie does not proclaim the truth about two love-struck somebodies , but permits them time and space to convince us of that all on their own .'</li></ul> | |
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| 1 | <ul><li>"the fact that it is n't very good is almost beside the point ."</li><li>'what starts off as a satisfying kids flck becomes increasingly implausible as it races through contrived plot points .'</li><li>'the film is ultimately about as inspiring as a hallmark card .'</li></ul> | |
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| 4 | <ul><li>'cool gadgets and creatures keep this fresh .'</li><li>'morton deserves an oscar nomination .'</li><li>'a brutal and funny work .'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.5380 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("vidhi0206/setfit-paraphrase-mpnet-sst5_v2") |
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# Run inference |
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preds = model("my response to the film is best described as lukewarm .") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 2 | 18.8062 | 52 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 64 | |
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| 1 | 64 | |
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| 2 | 64 | |
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| 3 | 64 | |
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| 4 | 64 | |
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### Training Hyperparameters |
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- batch_size: (8, 8) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-05 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0006 | 1 | 0.2259 | - | |
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| 0.0312 | 50 | 0.2373 | - | |
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| 0.0625 | 100 | 0.1726 | - | |
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| 0.0938 | 150 | 0.1607 | - | |
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| 0.125 | 200 | 0.1869 | - | |
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| 0.1562 | 250 | 0.1863 | - | |
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| 0.1875 | 300 | 0.224 | - | |
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| 0.2188 | 350 | 0.1625 | - | |
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| 0.25 | 400 | 0.1284 | - | |
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| 0.2812 | 450 | 0.1357 | - | |
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| 0.3125 | 500 | 0.2193 | - | |
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| 0.3438 | 550 | 0.1434 | - | |
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| 0.375 | 600 | 0.0524 | - | |
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| 0.4062 | 650 | 0.0558 | - | |
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| 0.4375 | 700 | 0.072 | - | |
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| 0.4688 | 750 | 0.0312 | - | |
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| 0.5 | 800 | 0.0732 | - | |
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| 0.5312 | 850 | 0.0117 | - | |
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| 0.5625 | 900 | 0.0311 | - | |
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| 0.5938 | 950 | 0.0228 | - | |
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| 0.625 | 1000 | 0.0026 | - | |
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| 0.6562 | 1050 | 0.0196 | - | |
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| 0.6875 | 1100 | 0.0017 | - | |
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| 0.7188 | 1150 | 0.0067 | - | |
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| 0.75 | 1200 | 0.0029 | - | |
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| 0.7812 | 1250 | 0.0041 | - | |
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| 0.8125 | 1300 | 0.0006 | - | |
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| 0.8438 | 1350 | 0.0022 | - | |
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| 0.875 | 1400 | 0.0006 | - | |
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| 0.9062 | 1450 | 0.0007 | - | |
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| 0.9375 | 1500 | 0.001 | - | |
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| 0.9688 | 1550 | 0.0009 | - | |
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| 1.0 | 1600 | 0.0013 | - | |
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### Framework Versions |
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- Python: 3.8.10 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.3.1 |
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- Transformers: 4.37.2 |
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- PyTorch: 2.2.0+cu121 |
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- Datasets: 2.17.0 |
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- Tokenizers: 0.15.1 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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