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--- |
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license: apache-2.0 |
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base_model: surrey-nlp/albert-large-v2-finetuned-abbDet |
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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: albert-large-v2-finetuned-abbDet-finetuned-ner |
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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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# albert-large-v2-finetuned-abbDet-finetuned-ner |
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This model is a fine-tuned version of [surrey-nlp/albert-large-v2-finetuned-abbDet](https://huggingface.co/surrey-nlp/albert-large-v2-finetuned-abbDet) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0950 |
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- Precision: 0.9784 |
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- Recall: 0.9763 |
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- F1: 0.9773 |
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- Accuracy: 0.9757 |
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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-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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: 10 |
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- mixed_precision_training: Native AMP |
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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 | 0.37 | 100 | 0.1655 | 0.9638 | 0.9621 | 0.9629 | 0.9622 | |
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| No log | 0.75 | 200 | 0.1073 | 0.9752 | 0.9705 | 0.9729 | 0.9709 | |
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| No log | 1.12 | 300 | 0.0951 | 0.9776 | 0.9742 | 0.9759 | 0.9740 | |
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| No log | 1.49 | 400 | 0.0952 | 0.9778 | 0.9752 | 0.9765 | 0.9748 | |
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| 0.1901 | 1.87 | 500 | 0.0948 | 0.9780 | 0.9745 | 0.9763 | 0.9746 | |
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| 0.1901 | 2.24 | 600 | 0.0947 | 0.9788 | 0.9758 | 0.9773 | 0.9755 | |
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| 0.1901 | 2.61 | 700 | 0.0962 | 0.9789 | 0.9766 | 0.9778 | 0.9758 | |
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| 0.1901 | 2.99 | 800 | 0.0950 | 0.9784 | 0.9763 | 0.9773 | 0.9757 | |
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| 0.1901 | 3.36 | 900 | 0.0984 | 0.9784 | 0.9763 | 0.9773 | 0.9755 | |
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| 0.0493 | 3.73 | 1000 | 0.1012 | 0.9781 | 0.9759 | 0.9770 | 0.9752 | |
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| 0.0493 | 4.1 | 1100 | 0.1029 | 0.9781 | 0.9763 | 0.9772 | 0.9754 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.15.2 |
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