Instructions to use horsbug98/Part_2_XLM_Model_E1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use horsbug98/Part_2_XLM_Model_E1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="horsbug98/Part_2_XLM_Model_E1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("horsbug98/Part_2_XLM_Model_E1") model = AutoModelForQuestionAnswering.from_pretrained("horsbug98/Part_2_XLM_Model_E1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 1.0, | |
| "global_step": 1072, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.47, | |
| "learning_rate": 1.6007462686567165e-05, | |
| "loss": 2.3884, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 0.93, | |
| "learning_rate": 2.0149253731343284e-06, | |
| "loss": 1.3984, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "step": 1072, | |
| "total_flos": 2520207218718720.0, | |
| "train_loss": 1.8482726438721615, | |
| "train_runtime": 561.1453, | |
| "train_samples_per_second": 22.917, | |
| "train_steps_per_second": 1.91 | |
| } | |
| ], | |
| "max_steps": 1072, | |
| "num_train_epochs": 1, | |
| "total_flos": 2520207218718720.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |