Instructions to use therealcyberlord/fake-news-classification-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use therealcyberlord/fake-news-classification-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="therealcyberlord/fake-news-classification-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("therealcyberlord/fake-news-classification-distilbert") model = AutoModelForSequenceClassification.from_pretrained("therealcyberlord/fake-news-classification-distilbert") - Notebooks
- Google Colab
- Kaggle
File size: 796 Bytes
896e193 1b3735e 896e193 fac9a76 896e193 | 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 | {
"_name_or_path": "distilbert-base-uncased-finetuned-sst-2-english",
"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"finetuning_task": "sst-2",
"hidden_dim": 3072,
"id2label": {
"0": "Fake",
"1": "Real"
},
"initializer_range": 0.02,
"label2id": {
"Fake": 0,
"Real": 1
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"output_past": true,
"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": "4.20.1",
"vocab_size": 30522
}
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