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--- |
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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: parsbert-finetuned-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: fa |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9447937270415372 |
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- name: Recall |
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type: recall |
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value: 0.9486470191864382 |
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- name: F1 |
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type: f1 |
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value: 0.9467164522465448 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9598951738759165 |
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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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# parsbert-finetuned-pos |
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This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on the udpos28 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1385 |
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- Precision: 0.9448 |
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- Recall: 0.9486 |
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- F1: 0.9467 |
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- Accuracy: 0.9599 |
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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: 8 |
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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: 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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| 0.122 | 1.0 | 3103 | 0.1215 | 0.9363 | 0.9424 | 0.9394 | 0.9561 | |
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| 0.0735 | 2.0 | 6206 | 0.1297 | 0.9413 | 0.9474 | 0.9443 | 0.9582 | |
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| 0.0373 | 3.0 | 9309 | 0.1385 | 0.9448 | 0.9486 | 0.9467 | 0.9599 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.0 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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