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
File size: 1,412 Bytes
6de6427 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | {
"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
]
} |