Text Classification
Transformers
ONNX
Safetensors
PyTorch
English
bert
multi-label-classification
multi-class-classification
emotion
go_emotions
emotion-classification
sentiment-analysis
tensorflow
Eval Results (legacy)
text-embeddings-inference
Instructions to use logasanjeev/bert-emotion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use logasanjeev/bert-emotion-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="logasanjeev/bert-emotion-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("logasanjeev/bert-emotion-classifier") model = AutoModelForSequenceClassification.from_pretrained("logasanjeev/bert-emotion-classifier", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download model.safetensors from logasanjeev/bert-emotion-classifier: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/logasanjeev/bert-emotion-classifier/resolve/087f7cd3277b18d6982ec39f8e2391bd66bc2e72/model.safetensors
- Command line
-
hf download hf://logasanjeev/bert-emotion-classifier@087f7cd3277b18d6982ec39f8e2391bd66bc2e72/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/logasanjeev/bert-emotion-classifier/resolve/087f7cd3277b18d6982ec39f8e2391bd66bc2e72/model.safetensors
438 MB
- Xet hash:
- 789e5104a6982b74aadc9c5f62fdd64c2b2f4eb7eadd61a1d78c110986c2d131
- Size of remote file:
- 438 MB
- SHA256:
- 491a23b02d2e5898230899e96c99d8dd828ba61258fec0406c2ecc8258db0766
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