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
- Xet hash:
- 41d21ffab52f658e4a1f08cb2b0f0ea56d73cae6722a776eb8a90c8a6b04ea99
- Size of remote file:
- 543 MB
- SHA256:
- 18224fcd90ef6599d215503bf217e62ce91abff4d2ffa0e2838051b7509d66c2
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