Instructions to use xiazeng/sciverbinary-model_train_dev_data-biobertl-rationale_continue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiazeng/sciverbinary-model_train_dev_data-biobertl-rationale_continue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xiazeng/sciverbinary-model_train_dev_data-biobertl-rationale_continue")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiazeng/sciverbinary-model_train_dev_data-biobertl-rationale_continue") model = AutoModelForSequenceClassification.from_pretrained("xiazeng/sciverbinary-model_train_dev_data-biobertl-rationale_continue", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xiazeng/sciverbinary-model_train_dev_data-biobertl-rationale_continue: direct link, hf CLI and curl.
- Browser
- Download file 1.46 GB
-
https://huggingface.co/xiazeng/sciverbinary-model_train_dev_data-biobertl-rationale_continue/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://xiazeng/sciverbinary-model_train_dev_data-biobertl-rationale_continue@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/xiazeng/sciverbinary-model_train_dev_data-biobertl-rationale_continue/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.46 GB
- Xet hash:
- 1280347169949a8cf94e1a0406880d377a554c0616abc448893bfbe9a83ba971
- Size of remote file:
- 1.46 GB
- SHA256:
- ca817dcd7df219fa3b94a1c8ae202140e2ecf1c8e3c29eff4c7461e0e3a9dcdf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.