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")# 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") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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oid sha256:9faca9368337c954426cb94ac4bf2fc46ce0a40f63d23180d029119598249b3c
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size 1457252344
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