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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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base_model: sentence-transformers/all-mpnet-base-v2 |
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metrics: |
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- accuracy |
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widget: |
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- text: What is the primary difference between homomorphic encryption and multi-party |
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computation in the context of secure multi-party computation protocols? |
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- text: How do organizations balance the need for innovation with the potential risks |
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and unintended consequences of emerging technologies? |
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- text: How doCompaniesbalanceIndividualCreativitywithTeamCollaboration to driveInnovationinthe |
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WORKPlace? |
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- text: How do companies balance the need for innovation with the risk of disrupting |
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their existing business models? |
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- text: What is the primary application of Natural Language Processing (NLP) in Google's |
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BERT language model, and how does it utilize masked language modeling to improve |
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contextual understanding? |
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pipeline_tag: text-classification |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/all-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: 1.0 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-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:** 384 tokens |
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- **Number of Classes:** 2 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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| semantic | <ul><li>'How do artificial intelligence systems navigate the trade-off between simplicity and accuracy when modeling complex real-world phenomena?'</li><li>'How do complex systems, consisting of many interconnected components, give rise to emergent properties that cannot be predicted from the characteristics of their individual parts?'</li><li>'How do complex systems, such as those found in nature and human societies, exhibit emergent properties that arise from the interactions of individual components?'</li></ul> | |
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| lexical | <ul><li>'What is the primary difference between a generative adversarial network (GAN) and a variational autoencoder (VAE) in deep learning?'</li><li>'What is the primary difference between a Decision Tree and a Random Forest in Machine Learning, and how do they alleviate overfitting?'</li><li>'What is the primary difference between a Bayesian neural network and a traditional feedforward neural network in the context of machine learning?'</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** | 1.0 | |
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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("yaniseuranova/setfit-paraphrase-mpnet-base-v2-sst2") |
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# Run inference |
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preds = model("How doCompaniesbalanceIndividualCreativitywithTeamCollaboration to driveInnovationinthe WORKPlace?") |
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``` |
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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 | 5 | 18.8511 | 32 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| lexical | 23 | |
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| semantic | 24 | |
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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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| 0.0139 | 1 | 0.2662 | - | |
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| 0.6944 | 50 | 0.0007 | - | |
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| 1.0 | 72 | - | 0.0003 | |
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| 1.3889 | 100 | 0.0004 | - | |
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| 2.0 | 144 | - | 0.0001 | |
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| 2.0833 | 150 | 0.0003 | - | |
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| 2.7778 | 200 | 0.0002 | - | |
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| 3.0 | 216 | - | 0.0001 | |
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| 3.4722 | 250 | 0.0002 | - | |
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| **4.0** | **288** | **-** | **0.0001** | |
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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: 2.6.1 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.3.0+cu121 |
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- Datasets: 2.18.0 |
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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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