Text Classification
Transformers
PyTorch
TensorBoard
mpnet
Generated from Trainer
text-embeddings-inference
Instructions to use mtyrrell/CPU_Conditional_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtyrrell/CPU_Conditional_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mtyrrell/CPU_Conditional_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mtyrrell/CPU_Conditional_Classifier") model = AutoModelForSequenceClassification.from_pretrained("mtyrrell/CPU_Conditional_Classifier") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
config.json
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"problem_type": "single_label_classification",
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float32",
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"transformers_version": "4.31.0",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float32",
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"transformers_version": "4.31.0",
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pytorch_model.bin
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runs/Jul21_12-04-04_14b8266d54f6/events.out.tfevents.1689941045.14b8266d54f6.4399.1
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tokenizer.json
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"version": "1.0",
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"truncation": {
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"max_length":
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"strategy": "LongestFirst",
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"Fixed":
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"Fixed": 128
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"direction": "Right",
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"pad_to_multiple_of": null,
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training_args.bin
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