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hmBERT-CoNLL-cp1

Token Classification
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PyTorch
bert
Generated from Trainer
Eval Results
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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: 0.8683215518658557
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+ - name: Recall
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+ type: recall
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+ value: 0.8889262874453047
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+ - name: F1
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+ type: f1
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+ value: 0.8785031185031185
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9809781550562673
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+ ---
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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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+
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+ # hmBERT-CoNLL-cp1
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+
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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: 0.0713
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+ - Precision: 0.8683
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+ - Recall: 0.8889
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+ - F1: 0.8785
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+ - Accuracy: 0.9810
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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