Instructions to use Jsevisal/balanced-augmented-ft-bert-large-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-ft-bert-large-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-ft-bert-large-gest-pred-seqeval-partialmatch")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/balanced-augmented-ft-bert-large-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/balanced-augmented-ft-bert-large-gest-pred-seqeval-partialmatch") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 20
Browse files
pytorch_model.bin
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runs/Apr19_10-09-51_8acf473b9751/events.out.tfevents.1681899007.8acf473b9751.707.4
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