Instructions to use javauser/nda_central_learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use javauser/nda_central_learning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="javauser/nda_central_learning")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("javauser/nda_central_learning") model = AutoModelForSequenceClassification.from_pretrained("javauser/nda_central_learning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,464 Bytes
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"dropout": 0.1,
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"id2label": {
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"succession": 42,
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},
"max_position_embeddings": 512,
"model_type": "distilbert",
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"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": "4.32.0.dev0",
"vocab_size": 30522
}
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