Instructions to use Jsevisal/balanced-augmented-ft-distilbert-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-distilbert-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-distilbert-gest-pred-seqeval-partialmatch")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/balanced-augmented-ft-distilbert-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/balanced-augmented-ft-distilbert-gest-pred-seqeval-partialmatch") - Notebooks
- Google Colab
- Kaggle
Training complete
Browse files
pytorch_model.bin
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runs/Mar24_11-28-10_38843543317f/events.out.tfevents.1679657296.38843543317f.785.4
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