Instructions to use monsoon-nlp/muril-adapted-local with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monsoon-nlp/muril-adapted-local with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="monsoon-nlp/muril-adapted-local")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/muril-adapted-local") model = AutoModelForMaskedLM.from_pretrained("monsoon-nlp/muril-adapted-local") - Notebooks
- Google Colab
- Kaggle
MuRIL - Unofficial
Multilingual Representations for Indian Languages : Google open sourced this BERT model pre-trained on 17 Indian languages, and their transliterated counterparts.
The model was trained using a self-supervised masked language modeling task. We do whole word masking with a maximum of 80 predictions. The model was trained for 1000K steps, with a batch size of 4096, and a max sequence length of 512.
Original model on TFHub: https://tfhub.dev/google/MuRIL/1
Official release now on HuggingFace (March 2021) https://huggingface.co/google/muril-base-cased
License: Apache 2.0
About this upload
I ported the TFHub .pb model to .h5 and then pytorch_model.bin for compatibility with Transformers.
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