Instructions to use yxLiao/scibert_ent_scierc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yxLiao/scibert_ent_scierc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="yxLiao/scibert_ent_scierc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("yxLiao/scibert_ent_scierc") model = AutoModel.from_pretrained("yxLiao/scibert_ent_scierc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
This is a span-based model for named entity recognition (NER). The model is trained on the SciERC dataset.
This repo contains:
- An encoder fine-tuned based on allenai/scibert_scivocab_uncased: pytorch_model.bin
- Components for NER: model_classifiers.pth
- Model training log: train.log
Codes are available in yxliao95/cxrgraph
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