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--- |
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language: |
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- en |
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license: apache-2.0 |
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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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datasets: |
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- sst2 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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widget: |
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- text: 'this is a story of two misfits who do n''t stand a chance alone , but together |
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they are magnificent . ' |
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- text: 'it does n''t believe in itself , it has no sense of humor ... it ''s just |
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plain bored . ' |
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- text: 'the band ''s courage in the face of official repression is inspiring , especially |
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for aging hippies ( this one included ) . ' |
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- text: 'a fast , funny , highly enjoyable movie . ' |
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- text: 'the movie achieves as great an impact by keeping these thoughts hidden as |
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... ( quills ) did by showing them . ' |
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pipeline_tag: text-classification |
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co2_eq_emissions: |
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emissions: 2.5933709269110308 |
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source: codecarbon |
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training_type: fine-tuning |
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on_cloud: false |
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cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K |
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ram_total_size: 31.777088165283203 |
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hours_used: 0.027 |
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hardware_used: 1 x NVIDIA GeForce RTX 3090 |
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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 on sst2 |
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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: sst2 |
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type: sst2 |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.8588082901554405 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 on sst2 |
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This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [sst2](https://huggingface.co/datasets/sst2) 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. For classification, it uses a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance. |
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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:** [sst2](https://huggingface.co/datasets/sst2) |
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- **Language:** en |
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- **License:** apache-2.0 |
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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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| negative | <ul><li>'stale and uninspired . '</li><li>"the film 's considered approach to its subject matter is too calm and thoughtful for agitprop , and the thinness of its characterizations makes it a failure as straight drama . ' "</li><li>"that their charm does n't do a load of good "</li></ul> | |
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| positive | <ul><li>"broomfield is energized by volletta wallace 's maternal fury , her fearlessness "</li><li>'flawless '</li><li>'insightfully written , delicately performed '</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** | 0.8588 | |
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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 🤗 Hub |
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model = SetFitModel.from_pretrained("tomaarsen/setfit-paraphrase-mpnet-base-v2-sst2-8-shot") |
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# Run inference |
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preds = model("a fast , funny , highly enjoyable movie . ") |
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``` |
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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 | 11.4375 | 33 | |
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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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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (10, 10) |
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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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- 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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| 0.1111 | 1 | 0.2126 | - | |
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| 1.1111 | 10 | 0.1604 | - | |
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| **2.2222** | **20** | **0.0224** | **0.1761** | |
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| 3.3333 | 30 | 0.0039 | - | |
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| 4.4444 | 40 | 0.0029 | 0.1935 | |
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| 5.5556 | 50 | 0.0026 | - | |
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| 6.6667 | 60 | 0.0008 | 0.1944 | |
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| 7.7778 | 70 | 0.0009 | - | |
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| 8.8889 | 80 | 0.0027 | 0.1941 | |
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| 10.0 | 90 | 0.0004 | - | |
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* The bold row denotes the saved checkpoint. |
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### Environmental Impact |
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Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
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- **Carbon Emitted**: 0.003 kg of CO2 |
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- **Hours Used**: 0.027 hours |
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### Training Hardware |
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- **On Cloud**: No |
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- **GPU Model**: 1 x NVIDIA GeForce RTX 3090 |
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- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K |
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- **RAM Size**: 31.78 GB |
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### Framework Versions |
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- Python: 3.9.16 |
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- SetFit: 1.0.0.dev0 |
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- Sentence Transformers: 2.2.2 |
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- Transformers: 4.29.0 |
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- PyTorch: 1.13.1+cu117 |
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- Datasets: 2.15.0 |
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- Tokenizers: 0.13.3 |
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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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