Instructions to use Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2 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-2 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-2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2") - Notebooks
- Google Colab
- Kaggle
Training complete
Browse files
pytorch_model.bin
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runs/Mar30_07-35-20_7e61e1ba55bf/events.out.tfevents.1680161733.7e61e1ba55bf.141.2
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