Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use TuringGame/distilbert-base-uncased-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TuringGame/distilbert-base-uncased-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TuringGame/distilbert-base-uncased-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TuringGame/distilbert-base-uncased-classifier") model = AutoModelForSequenceClassification.from_pretrained("TuringGame/distilbert-base-uncased-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Delete adapter_config.json
Browse files- adapter_config.json +0 -17
adapter_config.json
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{
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"auto_mapping": null,
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"base_model_name_or_path": "meta-llama/Llama-3.2-1B-Instruct",
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"encoder_dropout": 0.0,
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"encoder_hidden_size": 128,
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"encoder_num_layers": 2,
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"encoder_reparameterization_type": "MLP",
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"inference_mode": true,
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"num_attention_heads": 32,
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"num_layers": 16,
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"num_transformer_submodules": 1,
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"num_virtual_tokens": 20,
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"peft_type": "P_TUNING",
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"revision": null,
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"task_type": "CAUSAL_LM",
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"token_dim": 2048
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}
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