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")# 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 config.json from logasanjeev/bert-emotion-classifier: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
-
https://huggingface.co/logasanjeev/bert-emotion-classifier/resolve/1d9ee23e278eaf133ac817007e251bf0a78f8a63/config.json
- Command line
-
hf download hf://logasanjeev/bert-emotion-classifier@1d9ee23e278eaf133ac817007e251bf0a78f8a63/config.json
-
curl -L -o config.json https://huggingface.co/logasanjeev/bert-emotion-classifier/resolve/1d9ee23e278eaf133ac817007e251bf0a78f8a63/config.json
195 Bytes
| {"model_name": "bert-base-uncased", "num_labels": 28, "batch_size": 16, "max_length": 128, "epochs": 5, "learning_rate": 2e-05, "weight_decay": 0.01, "focal_loss_alpha": 1, "focal_loss_gamma": 2} |