| --- |
| license: apache-2.0 |
| base_model: bert-base-multilingual-cased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: ner-bert |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # ner-bert |
|
|
| This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0002 |
| - Precision: 1.0 |
| - Recall: 0.9993 |
| - F1: 0.9997 |
| - Accuracy: 1.0000 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 1 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0005 | 0.1 | 250 | 0.0047 | 0.9998 | 0.9861 | 0.9929 | 0.9994 | |
| | 0.009 | 0.2 | 500 | 0.0041 | 0.9961 | 0.9864 | 0.9912 | 0.9994 | |
| | 0.0004 | 0.3 | 750 | 0.0024 | 0.9977 | 0.9895 | 0.9936 | 0.9995 | |
| | 0.0001 | 0.4 | 1000 | 0.0010 | 0.9984 | 0.9975 | 0.9980 | 0.9999 | |
| | 0.0001 | 0.51 | 1250 | 0.0008 | 1.0 | 0.9975 | 0.9987 | 0.9999 | |
| | 0.0001 | 0.61 | 1500 | 0.0005 | 1.0 | 0.9975 | 0.9987 | 0.9999 | |
| | 0.0003 | 0.71 | 1750 | 0.0003 | 1.0 | 0.9991 | 0.9995 | 1.0000 | |
| | 0.0001 | 0.81 | 2000 | 0.0002 | 1.0 | 0.9993 | 0.9997 | 1.0000 | |
| | 0.0 | 0.91 | 2250 | 0.0002 | 1.0 | 0.9993 | 0.9997 | 1.0000 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.37.2 |
| - Pytorch 2.2.0+cu121 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
|
|
