Instructions to use Jsevisal/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/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/roberta-gest-pred-seqeval-partialmatch")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/roberta-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/roberta-gest-pred-seqeval-partialmatch") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91f715469c4f67a7ed9d23cdf2c9d0abb5412b2428dafb4ae5c3c68f76396952
- Size of remote file:
- 496 MB
- SHA256:
- c448e254e10fb94f7c92fbb62f4ff20f19454642359181f17c4fc6cc82f635f6
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