Instructions to use Laksh-Mendpara/MLOps-Assignment-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Laksh-Mendpara/MLOps-Assignment-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Laksh-Mendpara/MLOps-Assignment-3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Laksh-Mendpara/MLOps-Assignment-3") model = AutoModelForSequenceClassification.from_pretrained("Laksh-Mendpara/MLOps-Assignment-3") - Notebooks
- Google Colab
- Kaggle
Add tokenizer
Browse files- tokenizer.json +0 -0
- tokenizer_config.json +11 -9
tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"is_local": false,
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"mask_token": "
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"model_max_length": 512,
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"pad_token": "
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"sep_token": "
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"unk_token": "[UNK]"
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}
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": "<s>",
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"cls_token": "<s>",
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"eos_token": "</s>",
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"errors": "replace",
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"is_local": false,
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"mask_token": "<mask>",
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"model_max_length": 512,
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"tokenizer_class": "RobertaTokenizer",
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"trim_offsets": true,
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"unk_token": "<unk>"
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}
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