Text Classification
Transformers
PyTorch
English
roberta
emotions
multi-class-classification
multi-label-classification
text-embeddings-inference
Instructions to use Azma-AI/roberta-base-emotion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azma-AI/roberta-base-emotion-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Azma-AI/roberta-base-emotion-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Azma-AI/roberta-base-emotion-classifier") model = AutoModelForSequenceClassification.from_pretrained("Azma-AI/roberta-base-emotion-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Azma-AI/roberta-base-emotion-classifier: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Azma-AI/roberta-base-emotion-classifier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Azma-AI/roberta-base-emotion-classifier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Azma-AI/roberta-base-emotion-classifier/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- e443a993e6371cc8ab720171858f131ca53c36a5368406718144716e1d0bd04d
- Size of remote file:
- 499 MB
- SHA256:
- 4fd088956d38ce7ca956815b0203caf6f29b492b04c22c50d67542b3e02c449d
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