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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- caner |
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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-finetuned-ner-v4.009 |
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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: caner |
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type: caner |
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config: default |
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split: train[56%:57%] |
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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: 0.9051008303677343 |
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- name: Recall |
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type: recall |
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value: 0.8430939226519337 |
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- name: F1 |
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type: f1 |
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value: 0.8729977116704805 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9033665835411472 |
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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-finetuned-ner-v4.009 |
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the caner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8267 |
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- Precision: 0.9051 |
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- Recall: 0.8431 |
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- F1: 0.8730 |
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- Accuracy: 0.9034 |
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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: 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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| 0.2346 | 1.0 | 3228 | 0.6709 | 0.8530 | 0.8144 | 0.8332 | 0.8820 | |
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| 0.1486 | 2.0 | 6456 | 0.6723 | 0.9031 | 0.8243 | 0.8619 | 0.8975 | |
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| 0.0897 | 3.0 | 9684 | 0.8267 | 0.9051 | 0.8431 | 0.8730 | 0.9034 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.2 |
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