Instructions to use MilaNLProc/xlm-emo-t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MilaNLProc/xlm-emo-t with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MilaNLProc/xlm-emo-t")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/xlm-emo-t") model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/xlm-emo-t") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0a66f332c53d2cf5de536d7dab50edd588e9ae587609c5517c86c76f74000fb
- Size of remote file:
- 2.8 kB
- SHA256:
- 1ea9a704d4384aee27c5f145ce6f9f9343d2f2d8bf44d0f446a7b51393f7a854
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