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update model card README.md
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README.md
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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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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: insertion-prop05-ls01
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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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# insertion-prop05-ls01
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2120
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- Precision: 0.9800
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- Recall: 0.9776
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- F1: 0.9788
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- Accuracy: 0.9924
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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: 64
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- eval_batch_size: 64
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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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- label_smoothing_factor: 0.1
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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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| 0.2462 | 0.32 | 500 | 0.2160 | 0.9754 | 0.9697 | 0.9725 | 0.9902 |
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| 0.2194 | 0.64 | 1000 | 0.2128 | 0.9784 | 0.9763 | 0.9773 | 0.9919 |
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| 0.2171 | 0.96 | 1500 | 0.2120 | 0.9800 | 0.9776 | 0.9788 | 0.9924 |
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### Framework versions
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- Transformers 4.25.1
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- Pytorch 1.13.1+cu116
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- Datasets 2.8.0
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- Tokenizers 0.13.2
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