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--- |
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license: mit |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- id_nergrit_corpus |
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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: xlm-roberta-base-ner-silvanus |
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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: id_nergrit_corpus |
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type: id_nergrit_corpus |
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config: ner |
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split: validation |
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args: ner |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9014463504877228 |
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- name: Recall |
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type: recall |
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value: 0.9038785834738617 |
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- name: F1 |
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type: f1 |
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value: 0.9026608285618053 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9895516717325228 |
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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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# xlm-roberta-base-ner-silvanus |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the id_nergrit_corpus dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0457 |
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- Precision: 0.9014 |
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- Recall: 0.9039 |
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- F1: 0.9027 |
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- Accuracy: 0.9896 |
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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: 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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| 0.0492 | 1.0 | 1567 | 0.0410 | 0.8863 | 0.8938 | 0.8900 | 0.9886 | |
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| 0.0285 | 2.0 | 3134 | 0.0416 | 0.8941 | 0.9025 | 0.8983 | 0.9895 | |
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| 0.0159 | 3.0 | 4701 | 0.0457 | 0.9014 | 0.9039 | 0.9027 | 0.9896 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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