Instructions to use jubescoe/srini-trained-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jubescoe/srini-trained-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jubescoe/srini-trained-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jubescoe/srini-trained-model") model = AutoModelForSequenceClassification.from_pretrained("jubescoe/srini-trained-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ebf33c2344e2822ad5944712cbc82a92c59899a8229314fd8063470f583d74f
- Size of remote file:
- 438 MB
- SHA256:
- 9f99b55f672af7a2012b9bf724cc483d525d0ebb784f3d8676ccfea8797e5cbd
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