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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- udpos28 |
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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: udpos28-sm-all-POS |
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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: udpos28 |
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type: udpos28 |
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args: en |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9586517032792105 |
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- name: Recall |
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type: recall |
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value: 0.9588997472284696 |
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- name: F1 |
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type: f1 |
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value: 0.9587757092110369 |
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- name: Accuracy |
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type: accuracy |
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value: 0.964820639556654 |
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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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# udpos28-sm-all-POS |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the udpos28 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1479 |
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- Precision: 0.9587 |
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- Recall: 0.9589 |
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- F1: 0.9588 |
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- Accuracy: 0.9648 |
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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: 4 |
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- eval_batch_size: 4 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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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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| 0.1261 | 1.0 | 4978 | 0.1358 | 0.9513 | 0.9510 | 0.9512 | 0.9581 | |
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| 0.0788 | 2.0 | 9956 | 0.1326 | 0.9578 | 0.9578 | 0.9578 | 0.9642 | |
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| 0.0424 | 3.0 | 14934 | 0.1479 | 0.9587 | 0.9589 | 0.9588 | 0.9648 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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