Instructions to use subbareddyiiit/BERT-NLP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subbareddyiiit/BERT-NLP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subbareddyiiit/BERT-NLP")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subbareddyiiit/BERT-NLP") model = AutoModelForMaskedLM.from_pretrained("subbareddyiiit/BERT-NLP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from subbareddyiiit/BERT-NLP: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/subbareddyiiit/BERT-NLP/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://subbareddyiiit/BERT-NLP/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/subbareddyiiit/BERT-NLP/resolve/main/flax_model.msgpack
433 MB
- Xet hash:
- 0816fad35dd7b6ab45dfa3f7091ba2308f2a2e5c038bf55476b0fe87213184b2
- Size of remote file:
- 433 MB
- SHA256:
- 1c662aa1bdf27535de3af34b4ab437b1fa8845d9d0056baf9124aad582a836e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.