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Browse files- {1_Pooling โ my_first_test/1_Pooling}/config.json +0 -0
- my_first_test/README.md +49 -0
- config.json โ my_first_test/config.json +0 -0
- config_sentence_transformers.json โ my_first_test/config_sentence_transformers.json +0 -0
- model_head.pkl โ my_first_test/model_head.pkl +1 -1
- modules.json โ my_first_test/modules.json +0 -0
- pytorch_model.bin โ my_first_test/pytorch_model.bin +1 -1
- sentence_bert_config.json โ my_first_test/sentence_bert_config.json +0 -0
- special_tokens_map.json โ my_first_test/special_tokens_map.json +0 -0
- tokenizer.json โ my_first_test/tokenizer.json +0 -0
- tokenizer_config.json โ my_first_test/tokenizer_config.json +0 -0
- vocab.txt โ my_first_test/vocab.txt +0 -0
{1_Pooling โ my_first_test/1_Pooling}/config.json
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my_first_test/README.md
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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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# my_first_test
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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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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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## Usage
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To use this model for inference, first install the SetFit library:
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```bash
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python -m pip install setfit
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```
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You can then run inference as follows:
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```python
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from setfit import SetFitModel
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# Download from Hub and run inference
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model = SetFitModel.from_pretrained("my_first_test")
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# Run inference
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preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst ๐คฎ"])
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```
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## BibTeX entry and citation info
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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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config.json โ my_first_test/config.json
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config_sentence_transformers.json โ my_first_test/config_sentence_transformers.json
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model_head.pkl โ my_first_test/model_head.pkl
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modules.json โ my_first_test/modules.json
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pytorch_model.bin โ my_first_test/pytorch_model.bin
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sentence_bert_config.json โ my_first_test/sentence_bert_config.json
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special_tokens_map.json โ my_first_test/special_tokens_map.json
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tokenizer.json โ my_first_test/tokenizer.json
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tokenizer_config.json โ my_first_test/tokenizer_config.json
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vocab.txt โ my_first_test/vocab.txt
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