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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conll2003 |
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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: hmBERT-CoNLL-cp1 |
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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: conll2003 |
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type: conll2003 |
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args: conll2003 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8690143162744776 |
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- name: Recall |
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type: recall |
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value: 0.8887579939414338 |
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- name: F1 |
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type: f1 |
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value: 0.8787752724852317 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9810170943499085 |
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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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# hmBERT-CoNLL-cp1 |
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This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0710 |
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- Precision: 0.8690 |
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- Recall: 0.8888 |
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- F1: 0.8788 |
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- Accuracy: 0.9810 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 1 |
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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 | 0.06 | 25 | 0.4115 | 0.3593 | 0.3708 | 0.3649 | 0.9002 | |
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| No log | 0.11 | 50 | 0.2263 | 0.6360 | 0.6898 | 0.6618 | 0.9456 | |
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| No log | 0.17 | 75 | 0.1660 | 0.7250 | 0.7582 | 0.7412 | 0.9564 | |
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| No log | 0.23 | 100 | 0.1520 | 0.7432 | 0.7775 | 0.7600 | 0.9597 | |
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| No log | 0.28 | 125 | 0.1343 | 0.7683 | 0.8103 | 0.7888 | 0.9645 | |
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| No log | 0.34 | 150 | 0.1252 | 0.7973 | 0.8230 | 0.8099 | 0.9691 | |
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| No log | 0.4 | 175 | 0.1021 | 0.8118 | 0.8398 | 0.8255 | 0.9724 | |
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| No log | 0.46 | 200 | 0.1056 | 0.8153 | 0.8411 | 0.8280 | 0.9727 | |
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| No log | 0.51 | 225 | 0.0872 | 0.8331 | 0.8612 | 0.8469 | 0.9755 | |
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| No log | 0.57 | 250 | 0.1055 | 0.8226 | 0.8418 | 0.8321 | 0.9725 | |
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| No log | 0.63 | 275 | 0.0921 | 0.8605 | 0.8640 | 0.8623 | 0.9767 | |
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| No log | 0.68 | 300 | 0.0824 | 0.8600 | 0.8787 | 0.8692 | 0.9788 | |
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| No log | 0.74 | 325 | 0.0834 | 0.8530 | 0.8771 | 0.8649 | 0.9787 | |
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| No log | 0.8 | 350 | 0.0758 | 0.8646 | 0.8876 | 0.8759 | 0.9800 | |
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| No log | 0.85 | 375 | 0.0727 | 0.8705 | 0.8866 | 0.8784 | 0.9810 | |
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| No log | 0.91 | 400 | 0.0734 | 0.8717 | 0.8899 | 0.8807 | 0.9811 | |
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| No log | 0.97 | 425 | 0.0713 | 0.8683 | 0.8889 | 0.8785 | 0.9810 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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