Instructions to use VSPuzzler/SemevalClickbaitSpoilingNew with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VSPuzzler/SemevalClickbaitSpoilingNew with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VSPuzzler/SemevalClickbaitSpoilingNew")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VSPuzzler/SemevalClickbaitSpoilingNew") model = AutoModelForSequenceClassification.from_pretrained("VSPuzzler/SemevalClickbaitSpoilingNew") - Notebooks
- Google Colab
- Kaggle
Upload with huggingface_hub
Browse files- optimizer.pt +3 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- vocab.txt +0 -0
optimizer.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:46fccde32e059eb69dbd38023a823bc5cb3de5ec0cd2d588ed6cdd463f43f9f4
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size 535706209
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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:c87f6d241334b356090c76440b3b36cea76aae6714b059820ffc668909b57584
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size 267857393
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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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vocab.txt
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