Text Classification
Transformers
Safetensors
English
roberta
emotion-recognition
speech-emotion-recognition
english
affective-computing
umuteam
Eval Results (legacy)
text-embeddings-inference
Instructions to use UMUTeam/roberta-emotion-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMUTeam/roberta-emotion-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UMUTeam/roberta-emotion-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UMUTeam/roberta-emotion-en") model = AutoModelForSequenceClassification.from_pretrained("UMUTeam/roberta-emotion-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ad5a2692a0a9a255af3374831fa9c8c51a924b23e26c28c3a7eed251ef44a24
- Size of remote file:
- 5.78 kB
- SHA256:
- 50ff4d0b77f8b8413ffe66fc0f7944d4716a312e3cfbea2eeb4fc85c365f7a81
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.