gustavecortal commited on
Commit
4e9ef7f
1 Parent(s): 69efe87

Add SetFit model

Browse files
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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README.md ADDED
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+ ---
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+ license: apache-2.0
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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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+ pipeline_tag: text-classification
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+ ---
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+
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+ # gustavecortal/setfit-modality
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+
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+ This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
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+
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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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+
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+ ## Usage
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+
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+ To use this model for inference, first install the SetFit library:
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+
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+ ```bash
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+ python -m pip install setfit
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+ ```
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+
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+ You can then run inference as follows:
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from Hub and run inference
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+ model = SetFitModel.from_pretrained("gustavecortal/setfit-modality")
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+ # Run inference
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+ preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
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+ ```
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+
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+ ## BibTeX entry and citation info
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+
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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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+ ```
config.json ADDED
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+ {
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+ "_name_or_path": "/home/gcortal/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/",
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+ "architectures": [
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+ "BertModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 384,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 1536,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 6,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.28.1",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30522
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+ }
config_sentence_transformers.json ADDED
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+ {
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+ "__version__": {
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+ "sentence_transformers": "2.0.0",
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+ "transformers": "4.6.1",
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+ "pytorch": "1.8.1"
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+ }
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+ }
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tokenizer.json ADDED
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tokenizer_config.json ADDED
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vocab.txt ADDED
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