Text Classification
Transformers
PyTorch
English
roberta
emotion-classification
tone-mapping
tonepilot
bert
quantized
optimized
text-embeddings-inference
Instructions to use sdurgi/bert_emotion_response_classifier_quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sdurgi/bert_emotion_response_classifier_quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sdurgi/bert_emotion_response_classifier_quantized")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sdurgi/bert_emotion_response_classifier_quantized") model = AutoModelForSequenceClassification.from_pretrained("sdurgi/bert_emotion_response_classifier_quantized") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80ff6c53bb944dfdf9ad2e9f7f314d35fc5f2a6f8b7bd9d8337b0404f1afab76
- Size of remote file:
- 499 MB
- SHA256:
- 8e75882957163a70c37446f69a40c193185df9ecca5bd3065dfecb71420e9dd2
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