Instructions to use dwmit/ja_classification_brl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dwmit/ja_classification_brl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dwmit/ja_classification_brl")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dwmit/ja_classification_brl") model = AutoModelForTokenClassification.from_pretrained("dwmit/ja_classification_brl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: ja_classification_brl | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # ja_classification_brl | |
| This model is a fine-tuned version of [dicta-il/BEREL_2.0](https://huggingface.co/dicta-il/BEREL_2.0) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0712 | |
| - Precision: 0.9846 | |
| - Recall: 0.9846 | |
| - F1: 0.9846 | |
| - Accuracy: 0.9846 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | No log | 1.0 | 1125 | 0.0522 | 0.9819 | 0.9819 | 0.9819 | 0.9819 | | |
| | No log | 2.0 | 2250 | 0.0490 | 0.9837 | 0.9837 | 0.9837 | 0.9837 | | |
| | No log | 3.0 | 3375 | 0.0481 | 0.9843 | 0.9843 | 0.9843 | 0.9843 | | |
| | No log | 4.0 | 4500 | 0.0514 | 0.9844 | 0.9844 | 0.9844 | 0.9844 | | |
| | No log | 5.0 | 5625 | 0.0548 | 0.9848 | 0.9848 | 0.9848 | 0.9848 | | |
| | No log | 6.0 | 6750 | 0.0587 | 0.9846 | 0.9846 | 0.9846 | 0.9846 | | |
| | No log | 7.0 | 7875 | 0.0636 | 0.9844 | 0.9844 | 0.9844 | 0.9844 | | |
| | No log | 8.0 | 9000 | 0.0669 | 0.9846 | 0.9846 | 0.9846 | 0.9846 | | |
| | No log | 9.0 | 10125 | 0.0685 | 0.9844 | 0.9844 | 0.9844 | 0.9844 | | |
| | No log | 10.0 | 11250 | 0.0712 | 0.9846 | 0.9846 | 0.9846 | 0.9846 | | |
| ### Framework versions | |
| - Transformers 4.28.1 | |
| - Pytorch 1.13.0+cu117 | |
| - Datasets 2.11.0 | |
| - Tokenizers 0.11.6 | |