| --- |
| license: mit |
| base_model: deepset/gbert-large |
| tags: |
| - generated_from_trainer |
| datasets: |
| - universal_dependencies |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: gbert-large-upos |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: universal_dependencies |
| type: universal_dependencies |
| config: de_gsd |
| split: validation |
| args: de_gsd |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.825291976991079 |
| - name: Recall |
| type: recall |
| value: 0.7826990832215603 |
| - name: F1 |
| type: f1 |
| value: 0.7912197452035137 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9413806706114398 |
| --- |
| |
| <!-- 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. --> |
|
|
| # gbert-large-upos |
|
|
| This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the universal_dependencies dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1996 |
| - Precision: 0.8253 |
| - Recall: 0.7827 |
| - F1: 0.7912 |
| - Accuracy: 0.9414 |
| |
| ## 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: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 8 |
| - 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 | 438 | 0.3197 | 0.8098 | 0.7291 | 0.7486 | 0.8936 | |
| | No log | 2.0 | 876 | 0.2261 | 0.8287 | 0.7679 | 0.7832 | 0.9269 | |
| | No log | 3.0 | 1314 | 0.1996 | 0.8253 | 0.7827 | 0.7912 | 0.9414 | |
| | No log | 4.0 | 1752 | 0.2183 | 0.8162 | 0.8006 | 0.8041 | 0.9435 | |
| | No log | 5.0 | 2190 | 0.2120 | 0.8198 | 0.8025 | 0.8074 | 0.9496 | |
| | No log | 6.0 | 2628 | 0.2339 | 0.8207 | 0.8068 | 0.8116 | 0.9489 | |
| | No log | 7.0 | 3066 | 0.2728 | 0.8156 | 0.8045 | 0.8071 | 0.9486 | |
| | No log | 8.0 | 3504 | 0.2790 | 0.8205 | 0.8110 | 0.8132 | 0.9527 | |
| | No log | 9.0 | 3942 | 0.2854 | 0.8306 | 0.8096 | 0.8146 | 0.9527 | |
| | No log | 10.0 | 4380 | 0.2906 | 0.8299 | 0.8115 | 0.8151 | 0.9534 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.42.4 |
| - Pytorch 2.4.0+cu121 |
| - Datasets 2.21.0 |
| - Tokenizers 0.19.1 |
| |
