gustavecortal
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Add SetFit model
Browse files- 1_Pooling/config.json +7 -0
- README.md +49 -0
- config.json +26 -0
- config_sentence_transformers.json +7 -0
- model_head.pkl +3 -0
- modules.json +20 -0
- pytorch_model.bin +3 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +15 -0
- vocab.txt +0 -0
1_Pooling/config.json
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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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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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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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# gustavecortal/setfit-nouns-empathy
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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("gustavecortal/setfit-nouns-empathy")
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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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## 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
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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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"pad_token_id": 0,
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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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}
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config_sentence_transformers.json
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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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model_head.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:042c520894ecb75dff1f675289e866a63a9b337e415575eae106ee0e45e72d56
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size 13175
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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},
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{
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.models.Normalize"
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}
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]
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:7ea0dd01332ec52db36545f719419c2a5bd85723a4fab7786f07fb00ac9f58d8
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size 90890157
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sentence_bert_config.json
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{
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"max_seq_length": 256,
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"do_lower_case": false
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}
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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