Trong-Nghia
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End of training
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README.md
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---
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license: apache-2.0
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base_model: bert-large-uncased
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- f1
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model-index:
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- name: bert-large-uncased-detect-dep-v8
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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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# bert-large-uncased-detect-dep-v8
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This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5303
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- Accuracy: 0.736
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- F1: 0.8128
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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-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: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| 0.6453 | 1.0 | 1502 | 0.5703 | 0.712 | 0.8035 |
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| 0.6275 | 2.0 | 3004 | 0.5234 | 0.747 | 0.8230 |
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| 0.6078 | 3.0 | 4506 | 0.5303 | 0.736 | 0.8128 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.0
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- Tokenizers 0.13.3
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pytorch_model.bin
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