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 thresholds.json from logasanjeev/bert-emotion-classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.41 kB
-
https://huggingface.co/logasanjeev/bert-emotion-classifier/resolve/087f7cd3277b18d6982ec39f8e2391bd66bc2e72/thresholds.json
- Command line
-
hf download hf://logasanjeev/bert-emotion-classifier@087f7cd3277b18d6982ec39f8e2391bd66bc2e72/thresholds.json
-
curl -L -o thresholds.json https://huggingface.co/logasanjeev/bert-emotion-classifier/resolve/087f7cd3277b18d6982ec39f8e2391bd66bc2e72/thresholds.json
1.41 kB
| { | |
| "emotion_labels": [ | |
| "admiration", | |
| "amusement", | |
| "anger", | |
| "annoyance", | |
| "approval", | |
| "caring", | |
| "confusion", | |
| "curiosity", | |
| "desire", | |
| "disappointment", | |
| "disapproval", | |
| "disgust", | |
| "embarrassment", | |
| "excitement", | |
| "fear", | |
| "gratitude", | |
| "grief", | |
| "joy", | |
| "love", | |
| "nervousness", | |
| "optimism", | |
| "pride", | |
| "realization", | |
| "relief", | |
| "remorse", | |
| "sadness", | |
| "surprise", | |
| "neutral" | |
| ], | |
| "thresholds": [ | |
| 0.5000000000000001, | |
| 0.45000000000000007, | |
| 0.45000000000000007, | |
| 0.3500000000000001, | |
| 0.40000000000000013, | |
| 0.40000000000000013, | |
| 0.45000000000000007, | |
| 0.3500000000000001, | |
| 0.6000000000000002, | |
| 0.3500000000000001, | |
| 0.40000000000000013, | |
| 0.5000000000000001, | |
| 0.5000000000000001, | |
| 0.45000000000000007, | |
| 0.3500000000000001, | |
| 0.6000000000000002, | |
| 0.3500000000000001, | |
| 0.40000000000000013, | |
| 0.5000000000000001, | |
| 0.45000000000000007, | |
| 0.45000000000000007, | |
| 0.30000000000000004, | |
| 0.5000000000000001, | |
| 0.40000000000000013, | |
| 0.3500000000000001, | |
| 0.5500000000000002, | |
| 0.40000000000000013, | |
| 0.3500000000000001 | |
| ] | |
| } |