Instructions to use ChrisUPM/BioBERT_Re_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChrisUPM/BioBERT_Re_trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ChrisUPM/BioBERT_Re_trained")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ChrisUPM/BioBERT_Re_trained") model = AutoModelForSequenceClassification.from_pretrained("ChrisUPM/BioBERT_Re_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ChrisUPM/BioBERT_Re_trained: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/ChrisUPM/BioBERT_Re_trained/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ChrisUPM/BioBERT_Re_trained/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ChrisUPM/BioBERT_Re_trained/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 3638a3ef83588579d712281b0ca826634aece6c86adafc2d027534d8682e7ca7
- Size of remote file:
- 433 MB
- SHA256:
- a9c2cf007e3343dfe88481c0836999b678993ec21a46c70d7072df11a0aa3384
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.