Instructions to use contemmcm/1a7d904346e7c4adfb1de13434e74811 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use contemmcm/1a7d904346e7c4adfb1de13434e74811 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="contemmcm/1a7d904346e7c4adfb1de13434e74811")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("contemmcm/1a7d904346e7c4adfb1de13434e74811") model = AutoModelForSequenceClassification.from_pretrained("contemmcm/1a7d904346e7c4adfb1de13434e74811", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- adf415c43d1b53e696e5f7564ed2c2f079096e651b5fa82e7500abc8b1583474
- Size of remote file:
- 5.97 kB
- SHA256:
- f23a3f951f3cb22fb06287e8cb6dc4fdf933bf89151610ffd07c6997abedd652
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