Instructions to use Jsevisal/balanced-augmented-bert-large-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-bert-large-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-bert-large-gest-pred-seqeval-partialmatch-2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/balanced-augmented-bert-large-gest-pred-seqeval-partialmatch-2") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/balanced-augmented-bert-large-gest-pred-seqeval-partialmatch-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 16
Browse files
pytorch_model.bin
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runs/Mar29_16-10-56_a4ed29bb0971/events.out.tfevents.1680106271.a4ed29bb0971.2568.2
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