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:
- 219d370825d5a851e7890d4cde607b12cd0e5d8e6398c422da197291b7f2ddd6
- Size of remote file:
- 599 MB
- SHA256:
- c3dd485928dedd2a98cf28f3f7f6f89a6c4e7fde2cfcb78cff4ae14994b83bd1
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