Instructions to use SickBoy/layoutlm_documents with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SickBoy/layoutlm_documents with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SickBoy/layoutlm_documents")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("SickBoy/layoutlm_documents") model = AutoModelForTokenClassification.from_pretrained("SickBoy/layoutlm_documents", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 1.0, | |
| "best_model_checkpoint": "layoutlmv3-finetuned-documents/checkpoint-500", | |
| "epoch": 500.0, | |
| "global_step": 500, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 100.0, | |
| "eval_accuracy": 0.9957081545064378, | |
| "eval_f1": 0.8571428571428571, | |
| "eval_loss": 0.02892388217151165, | |
| "eval_precision": 0.8571428571428571, | |
| "eval_recall": 0.8571428571428571, | |
| "eval_runtime": 0.2509, | |
| "eval_samples_per_second": 7.973, | |
| "eval_steps_per_second": 3.986, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 200.0, | |
| "eval_accuracy": 1.0, | |
| "eval_f1": 1.0, | |
| "eval_loss": 0.000514168874360621, | |
| "eval_precision": 1.0, | |
| "eval_recall": 1.0, | |
| "eval_runtime": 0.2357, | |
| "eval_samples_per_second": 8.485, | |
| "eval_steps_per_second": 4.242, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 300.0, | |
| "eval_accuracy": 1.0, | |
| "eval_f1": 1.0, | |
| "eval_loss": 0.00031278689857572317, | |
| "eval_precision": 1.0, | |
| "eval_recall": 1.0, | |
| "eval_runtime": 0.2494, | |
| "eval_samples_per_second": 8.019, | |
| "eval_steps_per_second": 4.01, | |
| "step": 300 | |
| }, | |
| { | |
| "epoch": 400.0, | |
| "eval_accuracy": 1.0, | |
| "eval_f1": 1.0, | |
| "eval_loss": 0.0002630261005833745, | |
| "eval_precision": 1.0, | |
| "eval_recall": 1.0, | |
| "eval_runtime": 0.2427, | |
| "eval_samples_per_second": 8.24, | |
| "eval_steps_per_second": 4.12, | |
| "step": 400 | |
| }, | |
| { | |
| "epoch": 500.0, | |
| "learning_rate": 7.500000000000001e-06, | |
| "loss": 0.042, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 500.0, | |
| "eval_accuracy": 1.0, | |
| "eval_f1": 1.0, | |
| "eval_loss": 0.0001811975089367479, | |
| "eval_precision": 1.0, | |
| "eval_recall": 1.0, | |
| "eval_runtime": 0.2418, | |
| "eval_samples_per_second": 8.272, | |
| "eval_steps_per_second": 4.136, | |
| "step": 500 | |
| } | |
| ], | |
| "max_steps": 2000, | |
| "num_train_epochs": 2000, | |
| "total_flos": 659007452160000.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |