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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-multilingual-uncased |
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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: bert-base-multilingual-uncased-finetuned-ner-harem |
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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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# bert-base-multilingual-uncased-finetuned-ner-harem |
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1861 |
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- Precision: 0.7833 |
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- Recall: 0.7589 |
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- F1: 0.7709 |
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- Accuracy: 0.9634 |
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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: 100 |
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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 | 1.0 | 282 | 0.2275 | 0.5847 | 0.6014 | 0.5929 | 0.9378 | |
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| 0.2687 | 2.0 | 564 | 0.1620 | 0.7389 | 0.6754 | 0.7057 | 0.9583 | |
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| 0.2687 | 3.0 | 846 | 0.1395 | 0.7820 | 0.7446 | 0.7628 | 0.9659 | |
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| 0.0845 | 4.0 | 1128 | 0.1694 | 0.7458 | 0.7351 | 0.7404 | 0.9586 | |
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| 0.0845 | 5.0 | 1410 | 0.1861 | 0.7833 | 0.7589 | 0.7709 | 0.9634 | |
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| 0.0398 | 6.0 | 1692 | 0.1821 | 0.7583 | 0.7637 | 0.7610 | 0.9639 | |
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| 0.0398 | 7.0 | 1974 | 0.2303 | 0.7789 | 0.7064 | 0.7409 | 0.9595 | |
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| 0.0203 | 8.0 | 2256 | 0.1912 | 0.7350 | 0.7876 | 0.7604 | 0.9629 | |
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| 0.0109 | 9.0 | 2538 | 0.2304 | 0.7524 | 0.7613 | 0.7568 | 0.9595 | |
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| 0.0109 | 10.0 | 2820 | 0.2457 | 0.7617 | 0.7399 | 0.7506 | 0.9622 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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