Text Classification
Transformers
Safetensors
English
distilbert
emotion-detection
Eval Results (legacy)
text-embeddings-inference
Instructions to use YamenRM/Emotion_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YamenRM/Emotion_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YamenRM/Emotion_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YamenRM/Emotion_model") model = AutoModelForSequenceClassification.from_pretrained("YamenRM/Emotion_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +16 -18
config.json
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"dropout": 0.1,
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"dtype": "float32",
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"hidden_dim": 3072,
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"id2label": {
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"0": "LABEL_0",
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"1": "LABEL_1",
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"2": "LABEL_2",
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"3": "LABEL_3",
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"4": "LABEL_4",
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"5": "LABEL_5"
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},
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"initializer_range": 0.02,
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1,
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"LABEL_2": 2,
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"LABEL_3": 3,
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"LABEL_4": 4,
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"LABEL_5": 5
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"transformers_version": "4.56.1",
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"vocab_size": 30522
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"dropout": 0.1,
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"dtype": "float32",
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"hidden_dim": 3072,
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"initializer_range": 0.02,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"transformers_version": "4.56.1",
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"vocab_size": 30522,
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"id2label": {
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"0": "sadness",
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"1": "joy",
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"2": "love",
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"3": "anger",
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"4": "fear",
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"5": "surprise"
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},
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"label2id": {
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"sadness": 0,
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"joy": 1,
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"love": 2,
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"anger": 3,
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"fear": 4,
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"surprise
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