Instructions to use NTCAL/SavedAfterTrainingTest39 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTCAL/SavedAfterTrainingTest39 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NTCAL/SavedAfterTrainingTest39")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NTCAL/SavedAfterTrainingTest39") model = AutoModelForSequenceClassification.from_pretrained("NTCAL/SavedAfterTrainingTest39") - Notebooks
- Google Colab
- Kaggle
Commit ·
566130d
1
Parent(s): a15829e
update model card README.md
Browse files
README.md
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@@ -35,10 +35,13 @@ The following hyperparameters were used during training:
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 1
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- num_epochs: 3
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### Training results
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 4
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 1
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- num_epochs: 3
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- mixed_precision_training: Native AMP
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### Training results
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