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---
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base_model: sentence-transformers/paraphrase-mpnet-base-v2
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datasets:
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- SetFit/SentEval-CR
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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: you can take pic of your friends and the picture will pop up when they call
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.
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- text: the speakerphone , the radio , all features work perfectly .
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- text: 'a ) the picture quality ( color and sharpness of focusing ) are so great
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, it completely eliminated my doubt about digital imaging -- - how could one eat
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rice one grain at a time : - ) )'
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- text: so far the dvd works so i hope it does n 't break down like the reviews i
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've read .
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- text: i have a couple hundred contacts and the menu loads within a few seconds ,
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no big deal .
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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: SetFit/SentEval-CR
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type: SetFit/SentEval-CR
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split: test
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metrics:
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- type: accuracy
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value: 0.8698539176626826
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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 trained on the [SetFit/SentEval-CR](https://huggingface.co/datasets/SetFit/SentEval-CR) dataset 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:** 2 classes
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- **Training Dataset:** [SetFit/SentEval-CR](https://huggingface.co/datasets/SetFit/SentEval-CR)
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<!-- - **Language:** Unknown -->
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<!-- - **License:** 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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| 1 | <ul><li>'* slick-looking design and improved interface'</li><li>'as for bluetooth , no problems at all .'</li><li>'2 ) storage capacity'</li></ul> |
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| 0 | <ul><li>"the day finally arrived when i was sure i 'd leave sprint ."</li><li>"neither message was answered ( they ask for 24 hours before replying - i 've been waiting 27 days . )"</li><li>'only problem is that is a bit heavy .'</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** | 0.8699 |
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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("ellarose/my-awesome-setfit-model")
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# Run inference
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preds = model("the speakerphone , the radio , all features work perfectly .")
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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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<!--
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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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<!--
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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 | 4 | 18.0625 | 44 |
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| Label | Training Sample Count |
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|:------|:----------------------|
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| 0 | 7 |
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| 1 | 9 |
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### Training Hyperparameters
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- batch_size: (16, 16)
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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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| 0.025 | 1 | 0.2289 | - |
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### Framework Versions
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- Python: 3.11.9
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- SetFit: 1.0.3
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- Sentence Transformers: 2.7.0
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- Transformers: 4.39.0
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- PyTorch: 2.4.0+cpu
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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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