Instructions to use KalaiselvanD/fine_tuned_model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/fine_tuned_model_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/fine_tuned_model_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/fine_tuned_model_2") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/fine_tuned_model_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7ff66e48f0d72cea0f7780363e231b27244a1603128714906cd77da1b6d7259
- Size of remote file:
- 46.7 MB
- SHA256:
- eebb6a33de823adcd1caf3e7590a1215dc1c4903fc9304949a715658feddb5d3
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