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--- |
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license: mit |
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base_model: FacebookAI/xlm-roberta-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wikiann |
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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: fine_tuned_XLMROBERTA_cs_wikann |
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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: wikiann |
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type: wikiann |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.920336 |
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- name: Recall |
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type: recall |
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value: 0.934218 |
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- name: F1 |
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type: f1 |
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value: 0.927225 |
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- name: Accuracy |
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type: accuracy |
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value: 0.973202 |
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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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# fine_tuned_XLMROBERTA_cs_wikann |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on a czech wikiann dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1543 |
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- Precision: 0.9203 |
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- Recall: 0.9342 |
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- F1: 0.9272 |
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- Accuracy: 0.9732 |
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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: 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: 5 |
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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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| 0.51 | 0.27 | 500 | 0.1995 | 0.7873 | 0.8274 | 0.8069 | 0.9435 | |
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| 0.2164 | 0.53 | 1000 | 0.2216 | 0.7743 | 0.8430 | 0.8072 | 0.9407 | |
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| 0.1963 | 0.8 | 1500 | 0.1673 | 0.8465 | 0.8849 | 0.8653 | 0.9534 | |
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| 0.1478 | 1.07 | 2000 | 0.1612 | 0.8850 | 0.9 | 0.8925 | 0.9629 | |
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| 0.1316 | 1.33 | 2500 | 0.1508 | 0.8765 | 0.9081 | 0.8920 | 0.9615 | |
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| 0.1156 | 1.6 | 3000 | 0.1561 | 0.9028 | 0.9081 | 0.9054 | 0.9656 | |
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| 0.1069 | 1.87 | 3500 | 0.1544 | 0.9009 | 0.9091 | 0.9050 | 0.9651 | |
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| 0.0925 | 2.13 | 4000 | 0.1724 | 0.9008 | 0.9216 | 0.9111 | 0.9662 | |
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| 0.0791 | 2.4 | 4500 | 0.1385 | 0.9096 | 0.9201 | 0.9148 | 0.9705 | |
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| 0.0739 | 2.67 | 5000 | 0.1309 | 0.9130 | 0.9254 | 0.9192 | 0.9701 | |
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| 0.0732 | 2.93 | 5500 | 0.1593 | 0.9035 | 0.9190 | 0.9112 | 0.9679 | |
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| 0.0538 | 3.2 | 6000 | 0.1550 | 0.9193 | 0.9309 | 0.9251 | 0.9722 | |
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| 0.0529 | 3.47 | 6500 | 0.1451 | 0.9112 | 0.9330 | 0.9220 | 0.9710 | |
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| 0.0521 | 3.73 | 7000 | 0.1510 | 0.9185 | 0.9323 | 0.9253 | 0.9721 | |
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| 0.0526 | 4.0 | 7500 | 0.1378 | 0.9173 | 0.9325 | 0.9249 | 0.9727 | |
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| 0.0377 | 4.27 | 8000 | 0.1501 | 0.9164 | 0.9344 | 0.9253 | 0.9728 | |
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| 0.0382 | 4.53 | 8500 | 0.1541 | 0.9213 | 0.9352 | 0.9282 | 0.9729 | |
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| 0.0358 | 4.8 | 9000 | 0.1543 | 0.9203 | 0.9342 | 0.9272 | 0.9732 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
