Instructions to use abigailp/m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abigailp/m3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abigailp/m3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abigailp/m3") model = AutoModelForSequenceClassification.from_pretrained("abigailp/m3") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- pytorch_model.bin +1 -1
- runs/Jan31_12-54-12_f2ffbcdf767c/events.out.tfevents.1675169659.f2ffbcdf767c.234.9 +2 -2
- runs/Jan31_12-54-12_f2ffbcdf767c/events.out.tfevents.1675170597.f2ffbcdf767c.234.11 +3 -0
- runs/Jan31_13-16-55_f2ffbcdf767c/1675171021.4716756/events.out.tfevents.1675171021.f2ffbcdf767c.234.13 +3 -0
- runs/Jan31_13-16-55_f2ffbcdf767c/events.out.tfevents.1675171021.f2ffbcdf767c.234.12 +3 -0
- training_args.bin +1 -1
pytorch_model.bin
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