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--- |
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license: mit |
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base_model: deepset/gbert-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- universal_dependencies |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: gbert-large-upos |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: universal_dependencies |
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type: universal_dependencies |
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config: de_gsd |
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split: validation |
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args: de_gsd |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.825291976991079 |
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- name: Recall |
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type: recall |
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value: 0.7826990832215603 |
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- name: F1 |
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type: f1 |
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value: 0.7912197452035137 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9413806706114398 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# gbert-large-upos |
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This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the universal_dependencies dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1996 |
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- Precision: 0.8253 |
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- Recall: 0.7827 |
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- F1: 0.7912 |
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- Accuracy: 0.9414 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 438 | 0.3197 | 0.8098 | 0.7291 | 0.7486 | 0.8936 | |
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| No log | 2.0 | 876 | 0.2261 | 0.8287 | 0.7679 | 0.7832 | 0.9269 | |
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| No log | 3.0 | 1314 | 0.1996 | 0.8253 | 0.7827 | 0.7912 | 0.9414 | |
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| No log | 4.0 | 1752 | 0.2183 | 0.8162 | 0.8006 | 0.8041 | 0.9435 | |
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| No log | 5.0 | 2190 | 0.2120 | 0.8198 | 0.8025 | 0.8074 | 0.9496 | |
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| No log | 6.0 | 2628 | 0.2339 | 0.8207 | 0.8068 | 0.8116 | 0.9489 | |
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| No log | 7.0 | 3066 | 0.2728 | 0.8156 | 0.8045 | 0.8071 | 0.9486 | |
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| No log | 8.0 | 3504 | 0.2790 | 0.8205 | 0.8110 | 0.8132 | 0.9527 | |
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| No log | 9.0 | 3942 | 0.2854 | 0.8306 | 0.8096 | 0.8146 | 0.9527 | |
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| No log | 10.0 | 4380 | 0.2906 | 0.8299 | 0.8115 | 0.8151 | 0.9534 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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