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 class_wise_metrics.csv from logasanjeev/bert-emotion-classifier: direct link, hf CLI and curl.
- Browser
- Download file 970 Bytes
-
https://huggingface.co/logasanjeev/bert-emotion-classifier/resolve/d178eaae20e984e5678db2172ff1bbe5df3b257b/class_wise_metrics.csv
- Command line
-
hf download hf://logasanjeev/bert-emotion-classifier@d178eaae20e984e5678db2172ff1bbe5df3b257b/class_wise_metrics.csv
-
curl -L -o class_wise_metrics.csv https://huggingface.co/logasanjeev/bert-emotion-classifier/resolve/d178eaae20e984e5678db2172ff1bbe5df3b257b/class_wise_metrics.csv
970 Bytes
| Emotion,F1 Score,Precision,Recall,Support | |
| admiration,0.6987,0.6649,0.7361,504 | |
| amusement,0.8071,0.7635,0.8561,264 | |
| anger,0.503,0.6176,0.4242,198 | |
| annoyance,0.3892,0.3297,0.475,320 | |
| approval,0.3915,0.2966,0.5755,351 | |
| caring,0.4473,0.5196,0.3926,135 | |
| confusion,0.4714,0.4861,0.4575,153 | |
| curiosity,0.5781,0.4442,0.8275,284 | |
| desire,0.5229,0.5714,0.4819,83 | |
| disappointment,0.3333,0.2906,0.3907,151 | |
| disapproval,0.4323,0.3405,0.5918,267 | |
| disgust,0.4926,0.625,0.4065,123 | |
| embarrassment,0.4912,0.7,0.3784,37 | |
| excitement,0.4571,0.4486,0.466,103 | |
| fear,0.586,0.4599,0.8077,78 | |
| gratitude,0.9102,0.945,0.8778,352 | |
| grief,0.3333,0.3333,0.3333,6 | |
| joy,0.6135,0.6061,0.6211,161 | |
| love,0.8065,0.7826,0.8319,238 | |
| nervousness,0.4348,0.4348,0.4348,23 | |
| optimism,0.5564,0.5436,0.5699,186 | |
| pride,0.5217,0.8571,0.375,16 | |
| realization,0.2513,0.5217,0.1655,145 | |
| relief,0.5833,0.5385,0.6364,11 | |
| remorse,0.68,0.5426,0.9107,56 | |
| sadness,0.557,0.5845,0.5321,156 | |
| surprise,0.5562,0.4772,0.6667,141 | |
| neutral,0.6867,0.5821,0.8372,1787 | |