Instructions to use rmhirota/model_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rmhirota/model_dir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rmhirota/model_dir")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rmhirota/model_dir") model = AutoModelForSequenceClassification.from_pretrained("rmhirota/model_dir") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 71a58d3a1513e7e103e6f4f7a0f4c9697dd16dd5ed97d48f52e0985959074d7f
- Size of remote file:
- 436 MB
- SHA256:
- 334a77a03746e1636e38ea1c8c826466dd195e654cacd8a29be336d6442a07bd
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