Instructions to use xiazeng/sciverbinary-model_train_data-biobertb_wo-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_data-biobertb_wo-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_data-biobertb_wo-rationale_continue")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiazeng/sciverbinary-model_train_data-biobertb_wo-rationale_continue") model = AutoModelForSequenceClassification.from_pretrained("xiazeng/sciverbinary-model_train_data-biobertb_wo-rationale_continue") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f568b5224adb597cd29377bc2f4096c4d2930689f41d04f7bc27cf5eb6bee1b
- Size of remote file:
- 433 MB
- SHA256:
- 3b841ec1187a852abee885c1660002f54024c3269d3969c7a407ff12b6ebcda4
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