Instructions to use l3cube-pune/hindi-marathi-dev-bert-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hindi-marathi-dev-bert-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hindi-marathi-dev-bert-scratch")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hindi-marathi-dev-bert-scratch") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hindi-marathi-dev-bert-scratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
d3fb440
1
Parent(s): 111a2cf
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
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@@ -7,7 +7,7 @@
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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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"special_tokens_map_file": "
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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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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": "special_tokens_map.json",
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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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