Instructions to use k-partha/decision_bert_bio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use k-partha/decision_bert_bio with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="k-partha/decision_bert_bio")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("k-partha/decision_bert_bio") model = AutoModelForSequenceClassification.from_pretrained("k-partha/decision_bert_bio", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 888d0781a56077ee1e7e7b97e3877910a38394152007069338cf7b3d07e53984
- Size of remote file:
- 438 MB
- SHA256:
- 318f9bee779ff30644b35d540471707bbdb5dfcb13e9ead0ee39b3c1daded867
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.