Instructions to use hapandya/indic-hi-bn-MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hapandya/indic-hi-bn-MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hapandya/indic-hi-bn-MLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hapandya/indic-hi-bn-MLM") model = AutoModelForMaskedLM.from_pretrained("hapandya/indic-hi-bn-MLM") - Notebooks
- Google Colab
- Kaggle
Upload 4 files
Browse files- special_tokens_map.json +1 -0
- spiece.model +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
special_tokens_map.json
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{"bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}}
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spiece.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:3a1173c2b6e144a02c001e289a05b5dbefddf247c50d4dcf42633158b2968fcb
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size 5646064
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tokenizer.json
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tokenizer_config.json
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{"do_lower_case": true, "remove_space": true, "keep_accents": false, "bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "special_tokens_map_file": null, "name_or_path": "ai4bharat/indic-bert", "sp_model_kwargs": {}, "tokenizer_class": "AlbertTokenizer"}
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