Instructions to use alex2awesome/source-affiliation-model__basic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alex2awesome/source-affiliation-model__basic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alex2awesome/source-affiliation-model__basic")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alex2awesome/source-affiliation-model__basic") model = AutoModelForSequenceClassification.from_pretrained("alex2awesome/source-affiliation-model__basic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 3.0, | |
| "eval_steps": 200, | |
| "global_step": 363, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.65, | |
| "eval_accuracy": 0.7214611872146118, | |
| "eval_loss": 0.8989303708076477, | |
| "eval_macro_f1": 0.5005038448277949, | |
| "eval_micro_f1": 0.7214611872146118, | |
| "eval_runtime": 1.6443, | |
| "eval_samples_per_second": 266.38, | |
| "eval_steps_per_second": 2.433, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "step": 363, | |
| "total_flos": 2222916882315228.0, | |
| "train_loss": 0.9607851643207644, | |
| "train_runtime": 121.7053, | |
| "train_samples_per_second": 95.172, | |
| "train_steps_per_second": 2.983 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 363, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 500, | |
| "total_flos": 2222916882315228.0, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |