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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: cis-lmu/glot500-base |
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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: glot500_model_en_ewt |
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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: en_ewt |
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split: test |
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args: en_ewt |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9400958805311612 |
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- name: Recall |
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type: recall |
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value: 0.9420542470878327 |
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- name: F1 |
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type: f1 |
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value: 0.9410740449748372 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9483660387746274 |
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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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# glot500_model_en_ewt |
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This model is a fine-tuned version of [cis-lmu/glot500-base](https://huggingface.co/cis-lmu/glot500-base) on the universal_dependencies dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2101 |
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- Precision: 0.9401 |
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- Recall: 0.9421 |
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- F1: 0.9411 |
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- Accuracy: 0.9484 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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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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| 1.0519 | 1.0 | 625 | 0.2891 | 0.9291 | 0.9298 | 0.9295 | 0.9396 | |
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| 0.2366 | 2.0 | 1250 | 0.2101 | 0.9401 | 0.9421 | 0.9411 | 0.9484 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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