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--- |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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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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widget: |
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- text: "RT @Lrihendry: #TedCruz headed into the Presidential Debates. GO TED!! \n\ |
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\n#GOPDebates http://t.co/8S67pz8a4A" |
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- text: 'One thing in the debate was evident, apart from Trump, Rand Paul is the most |
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absurd choice for a candidate. #GOPDebate' |
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- text: "RT @aqv21: How #Hillary Looked When Watching #CarlyFiorina #GOPDebate #Carly2016\ |
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\ #tcot #pjnet #ccot #tlot #RedNationRising http://t.co/aYgMâ\x80¦" |
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- text: 'Who do you think won the #GOPDebate last night?' |
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- text: '@RealAlexJones @libertytarian @JakariJax @LeeAnnMcAdoo Wether @realDonaldTrump |
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is a trojan horse or not, is he worth a punt? #GOPDebate' |
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inference: true |
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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: 0.5306666666666666 |
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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:** 3 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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| Positive | <ul><li>'.@JohnKasich won this debate with a little home field advantage. #GOPDebates'</li><li>'RT @Mike_Surtel: @megynkelly your questions were more like attacks on @realDonaldTrump. Then u get upset when he got tough with u! What a jâ\x80¦'</li><li>'RT @kwrcrow: Congrats to @realDonaldTrump for your win in #GOPDebates polling last night. @Time @DRUDGE_REPORT Well done Sir! http://t.co/nâ\x80¦'</li></ul> | |
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| Neutral | <ul><li>'RT @CharleneCac: So does his position on Iran mean that Rick Perry is also pro-divestment from Israel? #GOPDebate'</li><li>"We Watched The Debate With A Bunch Of Conservative Activists. Here's How They Reacted #GOPDebate http://t.co/Ug21fI5FcE via @dailycaller"</li><li>"I loved the cluelessness of invoking Reagan's name on #IranDeal at #GOPDebate considering Reagan made deals w/ them."</li></ul> | |
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| Negative | <ul><li>"beeteedubs. If you have to play 'Lesser-of-17-Evils' with your party ... perhaps you need a new party. #p2 #tcot #GOPDebate"</li><li>"RT @Ornyadams: Single payer... no way! I would miss paying ten different bills after my annual physical. Where's the fun in writing one cheâ\x80¦"</li><li>"RT @madyclahane: srry rather not have decisions over my body being made by men that can't count to two #GOPDebate https://t.co/1Ps81yQaOl"</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** | 0.5307 | |
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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("subham18/setfit-paraphrase-mpnet-base-v2-twitter-sentiment") |
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# Run inference |
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preds = model("Who do you think won the #GOPDebate last night?") |
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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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*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 | 8 | 18.0833 | 25 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| Negative | 8 | |
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| Positive | 8 | |
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| Neutral | 8 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (4, 4) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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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: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:-------:|:------:|:-------------:|:---------------:| |
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| 0.0417 | 1 | 0.2934 | - | |
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| 1.0 | 24 | - | 0.263 | |
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| **2.0** | **48** | **-** | **0.2555** | |
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| 2.0833 | 50 | 0.0091 | - | |
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| 3.0 | 72 | - | 0.2598 | |
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| 4.0 | 96 | - | 0.261 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.12.3 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.4.0+cu121 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.15.2 |
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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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