Instructions to use fremy7/xlm_roberta_emotion_detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fremy7/xlm_roberta_emotion_detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fremy7/xlm_roberta_emotion_detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fremy7/xlm_roberta_emotion_detector") model = AutoModelForSequenceClassification.from_pretrained("fremy7/xlm_roberta_emotion_detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 478cd0ea88521635e924155eac5c1c2b2231b0c6d4906aac71dd1f732a329345
- Size of remote file:
- 2.22 GB
- SHA256:
- 3bf9c635d5a13af873f9a18e475188ce051c5ab54882ccf90c1e8e5695e3a382
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