Instructions to use vedanta2003/final_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vedanta2003/final_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vedanta2003/final_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vedanta2003/final_model") model = AutoModelForSequenceClassification.from_pretrained("vedanta2003/final_model") - Notebooks
- Google Colab
- Kaggle
Upload 2 files
Browse files- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
special_tokens_map.json
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
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tokenizer_config.json
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{"do_lower_case": false, "max_len": 512}
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