Instructions to use tmnam20/gpt1_n-layer-2_xnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmnam20/gpt1_n-layer-2_xnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmnam20/gpt1_n-layer-2_xnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmnam20/gpt1_n-layer-2_xnli") model = AutoModelForSequenceClassification.from_pretrained("tmnam20/gpt1_n-layer-2_xnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 1.1012382507324219, | |
| "best_model_checkpoint": "/kaggle/output/checkpoint-4000", | |
| "epoch": 0.16297262059973924, | |
| "eval_steps": 1000, | |
| "global_step": 4000, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.0, | |
| "learning_rate": 2.7777777777777777e-11, | |
| "loss": 1.1078, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 0.04, | |
| "learning_rate": 2.7750000000000004e-08, | |
| "loss": 1.128, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 0.04, | |
| "eval_accuracy": 0.330938123752495, | |
| "eval_loss": 1.1083118915557861, | |
| "eval_runtime": 12.3979, | |
| "eval_samples_per_second": 404.1, | |
| "eval_steps_per_second": 50.573, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 0.08, | |
| "learning_rate": 5.5527777777777784e-08, | |
| "loss": 1.1164, | |
| "step": 2000 | |
| }, | |
| { | |
| "epoch": 0.08, | |
| "eval_accuracy": 0.3323353293413174, | |
| "eval_loss": 1.102672815322876, | |
| "eval_runtime": 12.2861, | |
| "eval_samples_per_second": 407.778, | |
| "eval_steps_per_second": 51.033, | |
| "step": 2000 | |
| }, | |
| { | |
| "epoch": 0.12, | |
| "learning_rate": 8.327777777777778e-08, | |
| "loss": 1.1158, | |
| "step": 3000 | |
| }, | |
| { | |
| "epoch": 0.12, | |
| "eval_accuracy": 0.34211576846307384, | |
| "eval_loss": 1.1024774312973022, | |
| "eval_runtime": 12.3746, | |
| "eval_samples_per_second": 404.863, | |
| "eval_steps_per_second": 50.668, | |
| "step": 3000 | |
| }, | |
| { | |
| "epoch": 0.16, | |
| "learning_rate": 1.1105555555555557e-07, | |
| "loss": 1.1115, | |
| "step": 4000 | |
| }, | |
| { | |
| "epoch": 0.16, | |
| "eval_accuracy": 0.34890219560878244, | |
| "eval_loss": 1.1012382507324219, | |
| "eval_runtime": 12.5405, | |
| "eval_samples_per_second": 399.505, | |
| "eval_steps_per_second": 49.998, | |
| "step": 4000 | |
| } | |
| ], | |
| "logging_steps": 1000, | |
| "max_steps": 10000000, | |
| "num_train_epochs": 408, | |
| "save_steps": 1000, | |
| "total_flos": 1393758830592000.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |