Instructions to use Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9d8d4bba0887c4142e903395826aa4fcdc81abb3dc065b98886226add3faaf9
- Size of remote file:
- 2.11 MB
- SHA256:
- a60abad17917ed62a89b321bd425a54df25c2d9af89161f31a67bfefa04a9ac9
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