Instructions to use SuperSecureHuman/trainer_test_checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SuperSecureHuman/trainer_test_checkpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SuperSecureHuman/trainer_test_checkpoint")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SuperSecureHuman/trainer_test_checkpoint") model = AutoModelForSequenceClassification.from_pretrained("SuperSecureHuman/trainer_test_checkpoint") - Notebooks
- Google Colab
- Kaggle
File size: 834 Bytes
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"best_metric": 0.25,
"best_model_checkpoint": "./trainer_test_checkpoint/checkpoint-10",
"epoch": 0.2,
"global_step": 10,
"is_hyper_param_search": false,
"is_local_process_zero": true,
"is_world_process_zero": true,
"log_history": [
{
"epoch": 0.1,
"eval_accuracy": 0.17,
"eval_loss": 1.7631263732910156,
"eval_runtime": 3.5157,
"eval_samples_per_second": 28.444,
"eval_steps_per_second": 14.222,
"step": 5
},
{
"epoch": 0.2,
"eval_accuracy": 0.25,
"eval_loss": 1.7874902486801147,
"eval_runtime": 3.5523,
"eval_samples_per_second": 28.15,
"eval_steps_per_second": 14.075,
"step": 10
}
],
"max_steps": 100,
"num_train_epochs": 2,
"total_flos": 5262362849280.0,
"trial_name": null,
"trial_params": null
}
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