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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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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: ner_model |
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results: [] |
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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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# ner_model |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1247 |
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- Precision: 0.8073 |
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- Recall: 0.8695 |
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- F1: 0.8372 |
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- Accuracy: 0.9655 |
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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: 1e-05 |
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- train_batch_size: 16 |
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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: 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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| No log | 0.4 | 100 | 0.5360 | 0.4604 | 0.4644 | 0.4624 | 0.8846 | |
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| No log | 0.81 | 200 | 0.2882 | 0.6137 | 0.6619 | 0.6369 | 0.9307 | |
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| No log | 1.21 | 300 | 0.2128 | 0.7236 | 0.7649 | 0.7437 | 0.9442 | |
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| No log | 1.62 | 400 | 0.1811 | 0.7146 | 0.7925 | 0.7515 | 0.9494 | |
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| 0.4608 | 2.02 | 500 | 0.1594 | 0.7369 | 0.8021 | 0.7681 | 0.9542 | |
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| 0.4608 | 2.43 | 600 | 0.1532 | 0.7494 | 0.8331 | 0.7890 | 0.9572 | |
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| 0.4608 | 2.83 | 700 | 0.1403 | 0.7660 | 0.8417 | 0.8021 | 0.9594 | |
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| 0.4608 | 3.24 | 800 | 0.1342 | 0.7909 | 0.8428 | 0.8160 | 0.9625 | |
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| 0.4608 | 3.64 | 900 | 0.1325 | 0.7867 | 0.8572 | 0.8204 | 0.9626 | |
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| 0.1256 | 4.05 | 1000 | 0.1275 | 0.8056 | 0.8632 | 0.8334 | 0.9648 | |
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| 0.1256 | 4.45 | 1100 | 0.1229 | 0.8131 | 0.8643 | 0.8379 | 0.9657 | |
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| 0.1256 | 4.86 | 1200 | 0.1247 | 0.8073 | 0.8695 | 0.8372 | 0.9655 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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