Instructions to use Mukundhan32/testmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mukundhan32/testmodel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mukundhan32/testmodel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mukundhan32/testmodel") model = AutoModelForSequenceClassification.from_pretrained("Mukundhan32/testmodel") - Notebooks
- Google Colab
- Kaggle
| tf_model[[:space:]](2).h5 filter=lfs diff=lfs merge=lfs -text | |
| tf_model.h5 filter=lfs diff=lfs merge=lfs -text | |
| pytorch_model.bin filter=lfs diff=lfs merge=lfs -text | |
| model[[:space:]](3).safetensors filter=lfs diff=lfs merge=lfs -text | |
| model.safetensors filter=lfs diff=lfs merge=lfs -text | |