Instructions to use sschet/scibert_scivocab_cased_ner_jnlpba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sschet/scibert_scivocab_cased_ner_jnlpba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sschet/scibert_scivocab_cased_ner_jnlpba")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sschet/scibert_scivocab_cased_ner_jnlpba") model = AutoModelForTokenClassification.from_pretrained("sschet/scibert_scivocab_cased_ner_jnlpba") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d856bffb801ce51fc185afc0e587b9fb83b8887ace555227a2d1806c0848e521
- Size of remote file:
- 440 MB
- SHA256:
- f318c0c9452000f211edc4bc5b7eb0fea906e55544af8004d3ab09cea02924eb
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