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--- |
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license: mit |
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base_model: prajjwal1/bert-tiny |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: mva_ner_2 |
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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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# mva_ner_2 |
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0026 |
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- Overall Precision: 0.9873 |
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- Overall Recall: 0.9873 |
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- Overall F1: 0.9873 |
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- Overall Accuracy: 0.9987 |
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- Year F1: 1.0 |
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- Years Ago F1: 0.9844 |
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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: 0.001 |
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- train_batch_size: 16 |
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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: 500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Year F1 | Years Ago F1 | |
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|:-------------:|:------:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------:|:------------:| |
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| 0.0099 | 35.71 | 1000 | 0.0225 | 0.9625 | 0.9747 | 0.9686 | 0.9960 | 1.0 | 0.9612 | |
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| 0.0078 | 71.43 | 2000 | 0.0157 | 0.9625 | 0.9747 | 0.9686 | 0.9960 | 1.0 | 0.9612 | |
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| 0.0078 | 107.14 | 3000 | 0.0075 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 | |
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| 0.0061 | 142.86 | 4000 | 0.0062 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 | |
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| 0.0053 | 178.57 | 5000 | 0.0032 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 | |
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| 0.0049 | 214.29 | 6000 | 0.0179 | 0.9747 | 0.9747 | 0.9747 | 0.9973 | 1.0 | 0.9688 | |
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| 0.0049 | 250.0 | 7000 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0034 | 285.71 | 8000 | 0.0064 | 0.9747 | 0.9747 | 0.9747 | 0.9973 | 1.0 | 0.9688 | |
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| 0.0037 | 321.43 | 9000 | 0.0148 | 0.9875 | 1.0 | 0.9937 | 0.9987 | 1.0 | 0.9922 | |
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| 0.0035 | 357.14 | 10000 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.003 | 392.86 | 11000 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0028 | 428.57 | 12000 | 0.0032 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 | |
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| 0.0025 | 464.29 | 13000 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0024 | 500.0 | 14000 | 0.0026 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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