Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use tamarab/bert-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tamarab/bert-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tamarab/bert-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tamarab/bert-emotion") model = AutoModelForSequenceClassification.from_pretrained("tamarab/bert-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 246185d1f19506e9cfd6cfef36d451b43b9963dadb781755000caaa6afaa6c45
- Size of remote file:
- 263 MB
- SHA256:
- 813d4e42b9a3f77dcfe3511134461f3f43941a14f4b260dcf89aae7c93ad56b7
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