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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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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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+ # model_4000_2/model
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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("model_4000_2/model")
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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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+ "_name_or_path": "/home/yjmsvma/.cache/torch/sentence_transformers/distilbert_distilbert-base-multilingual-cased",
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+ "activation": "gelu",
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+ "architectures": [
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+ "DistilBertModel"
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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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