Text Classification
Transformers
Safetensors
PyTorch
English
modernbert
ModernBERT
emotions
multi-class-classification
multi-label-classification
text-embeddings-inference
Instructions to use cirimus/modernbert-base-go-emotions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cirimus/modernbert-base-go-emotions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cirimus/modernbert-base-go-emotions")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cirimus/modernbert-base-go-emotions") model = AutoModelForSequenceClassification.from_pretrained("cirimus/modernbert-base-go-emotions") - Inference
- Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 5b65f306bdf2d0aa6bac29672d19673a67176ef5c6989abd9408339f419f2c32
- Size of remote file:
- 368 kB
- SHA256:
- dc378d522eeff71e8a6b09b090986f7fab4a7a45f9409d4e6ea21aebed7a3e32
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