Instructions to use contemmcm/a58af9b811decb15de671183c4b54b07 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use contemmcm/a58af9b811decb15de671183c4b54b07 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="contemmcm/a58af9b811decb15de671183c4b54b07")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("contemmcm/a58af9b811decb15de671183c4b54b07") model = AutoModelForSequenceClassification.from_pretrained("contemmcm/a58af9b811decb15de671183c4b54b07", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 553e53e96e22a44cf893ec1e5cc52022213c9136eca6ed3b7f4936967c2fffe4
- Size of remote file:
- 5.97 kB
- SHA256:
- 59731f4e506d437edb72497c70f4ed918456a8dcb91482e6a0760150dbe925cd
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