Instructions to use dwmit/ja_classification_brl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dwmit/ja_classification_brl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dwmit/ja_classification_brl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dwmit/ja_classification_brl") model = AutoModelForTokenClassification.from_pretrained("dwmit/ja_classification_brl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 500
Browse files- pytorch_model.bin +1 -1
- runs/Jul07_12-15-56_eimtest-ESC4000-G4/events.out.tfevents.1688721387.eimtest-ESC4000-G4.1618890.0 +2 -2
- runs/Jul07_12-15-56_eimtest-ESC4000-G4/events.out.tfevents.1688723240.eimtest-ESC4000-G4.1618890.2 +3 -0
- runs/Jul07_13-21-18_eimtest-ESC4000-G4/1688725287.2190282/events.out.tfevents.1688725287.eimtest-ESC4000-G4.1623841.1 +3 -0
- runs/Jul07_13-21-18_eimtest-ESC4000-G4/events.out.tfevents.1688725287.eimtest-ESC4000-G4.1623841.0 +3 -0
- training_args.bin +1 -1
pytorch_model.bin
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