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:
- 28ef0f157b2f587c224a23f503dd29207cfc284d17712b50c68296b306b8ec27
- Size of remote file:
- 17.1 MB
- SHA256:
- f59925fcb90c92b894cb93e51bb9b4a6105c5c249fe54ce1c704420ac39b81af
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