Text Classification
Transformers
Safetensors
English
bert
emotion-classification
multi-label
goemotions
contrastive-learning
tri-tower
Eval Results (legacy)
Instructions to use sdeakin/fine_tuned_bert_emotions_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sdeakin/fine_tuned_bert_emotions_large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sdeakin/fine_tuned_bert_emotions_large")# Load model directly from transformers import AutoTokenizer, MultiLabelBert tokenizer = AutoTokenizer.from_pretrained("sdeakin/fine_tuned_bert_emotions_large") model = MultiLabelBert.from_pretrained("sdeakin/fine_tuned_bert_emotions_large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 333192594525d54bb3b5297899f20c227e883f9022976c8467c6d93b28438360
- Size of remote file:
- 5.78 kB
- SHA256:
- b1d3e61ec17c0604b6d0547c3050c0bd7f3670844b53be06f9f3077c6d7431ee
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