Fill-Mask
Transformers
Safetensors
Bengali
distilbert
bengali
bangla
BnLM-F
bnlm
bangla-model
small-bangla-model
bengali-nlp
bnlp
low-resource
masked-language-modeling
pretrained
language-model
Instructions to use nahid-hub/BnLM-F-135m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nahid-hub/BnLM-F-135m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nahid-hub/BnLM-F-135m")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nahid-hub/BnLM-F-135m") model = AutoModelForMaskedLM.from_pretrained("nahid-hub/BnLM-F-135m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,136 Bytes
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"added_tokens_decoder": {
"0": {
"content": "[UNK]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "[CLS]",
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"special": true
},
"2": {
"content": "[SEP]",
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"special": true
},
"3": {
"content": "[PAD]",
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"rstrip": false,
"single_word": false,
"special": true
},
"4": {
"content": "[MASK]",
"lstrip": false,
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"single_word": false,
"special": true
}
},
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"tokenizer_class": "PreTrainedTokenizerFast",
"unk_token": "[UNK]"
}
|