Text Classification
Transformers
PyTorch
Safetensors
distilbert
Eval Results (legacy)
text-embeddings-inference
Instructions to use DracoHugging/Distilbert-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DracoHugging/Distilbert-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DracoHugging/Distilbert-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DracoHugging/Distilbert-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("DracoHugging/Distilbert-sentiment-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 981 Bytes
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"_name_or_path": "distilbert-base-uncased",
"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"id2label": {
"0": "Angry",
"1": "Curious to dive deeper",
"2": "Disgusted",
"3": "Fearful",
"4": "Happy",
"5": "Neutral",
"6": "Sad",
"7": "Surprised"
},
"initializer_range": 0.02,
"label2id": {
"Angry": 0,
"Curious to dive deeper": 1,
"Disgusted": 2,
"Fearful": 3,
"Happy": 4,
"Neutral": 5,
"Sad": 6,
"Surprised": 7
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"problem_type": "single_label_classification",
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"torch_dtype": "float32",
"transformers_version": "4.30.1",
"vocab_size": 30522
}
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