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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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- clareandme/multiLabelClassification |
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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: The AI and user talk about how sleep problems are affecting the user's daily |
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life. The AI suggests improvements like sticking to a regular sleep schedule, |
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establishing a bedtime routine, and reducing screen time before bed. The user |
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acknowledges the challenge of implementing these changes but is willing to give |
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them a try for better sleep quality. |
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- text: The AI inquires about the user’s overall well-being and offers to talk about |
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managing work and study demands. The user reveals they’re feeling swamped by job |
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and exam pressures but find comfort in having a well-organized schedule. |
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- text: The AI and user talk about a recent falling out with a close friend who has |
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been giving them the cold shoulder. The user feels hurt and is uncertain about |
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the future of their friendship. |
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- text: The AI and user have a conversation about ways to manage and cope with the |
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loss of a loved partner. |
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- text: The AI engages the user in a conversation about their current challenges. |
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The user discloses that they’re feeling stressed and anxious due to financial |
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instability and rising debt. |
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inference: false |
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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: clareandme/multiLabelClassification |
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type: clareandme/multiLabelClassification |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.32142857142857145 |
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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 [clareandme/multiLabelClassification](https://huggingface.co/datasets/clareandme/multiLabelClassification) 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 OneVsRestClassifier 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 OneVsRestClassifier instance |
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- **Maximum Sequence Length:** 512 tokens |
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<!-- - **Number of Classes:** Unknown --> |
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- **Training Dataset:** [clareandme/multiLabelClassification](https://huggingface.co/datasets/clareandme/multiLabelClassification) |
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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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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.3214 | |
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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("clareandme/multilabel-setfit-model-v2") |
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# Run inference |
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preds = model("The AI and user have a conversation about ways to manage and cope with the loss of a loved partner.") |
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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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## 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 | 10 | 33.475 | 68 | |
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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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- num_iterations: 20 |
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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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| 0.0033 | 1 | 0.1896 | - | |
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| 0.1667 | 50 | 0.2453 | - | |
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| 0.3333 | 100 | 0.1182 | - | |
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| 0.5 | 150 | 0.2458 | - | |
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| 0.6667 | 200 | 0.0401 | - | |
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| 0.8333 | 250 | 0.0763 | - | |
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| 1.0 | 300 | 0.0915 | 0.1302 | |
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| 1.1667 | 350 | 0.1105 | - | |
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| 1.3333 | 400 | 0.0715 | - | |
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| 1.5 | 450 | 0.126 | - | |
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| 1.6667 | 500 | 0.1074 | - | |
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| 1.8333 | 550 | 0.0781 | - | |
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| 2.0 | 600 | 0.0608 | 0.1102 | |
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| 2.1667 | 650 | 0.1246 | - | |
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| 2.3333 | 700 | 0.0791 | - | |
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| 2.5 | 750 | 0.0662 | - | |
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| 2.6667 | 800 | 0.0906 | - | |
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| 2.8333 | 850 | 0.0763 | - | |
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| **3.0** | **900** | **0.0656** | **0.1026** | |
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| 3.1667 | 950 | 0.0476 | - | |
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| 3.3333 | 1000 | 0.1086 | - | |
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| 3.5 | 1050 | 0.0903 | - | |
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| 3.6667 | 1100 | 0.0552 | - | |
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| 3.8333 | 1150 | 0.0335 | - | |
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| 4.0 | 1200 | 0.0689 | 0.1028 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.12 |
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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: 2.3.1+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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