Text Classification
Transformers
Safetensors
modernbert
emotion
multi-label
goemotions
text-embeddings-inference
Instructions to use opus-research/opus-emotion-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use opus-research/opus-emotion-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="opus-research/opus-emotion-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("opus-research/opus-emotion-1") model = AutoModelForSequenceClassification.from_pretrained("opus-research/opus-emotion-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6f58b576f32ec30835e20ab405f4bdae4e9a0781b3313b1d8c4d8b4a342cac3
- Size of remote file:
- 5.2 kB
- SHA256:
- 101f0d160531f3d4c00276c605dab3605c537beb3895f364050b90e0b5f6226b
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