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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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datasets: |
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- glue |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: bert-base-sst-2 |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: glue |
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type: glue |
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config: sst2 |
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split: validation |
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args: sst2 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.930045871559633 |
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- name: F1 |
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type: f1 |
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value: 0.9299971705127952 |
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- name: Precision |
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type: precision |
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value: 0.9302394783826914 |
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- name: Recall |
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type: recall |
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value: 0.9298749684263703 |
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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-base-sst-2 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4216 |
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- Accuracy: 0.9300 |
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- F1: 0.9300 |
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- Precision: 0.9302 |
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- Recall: 0.9299 |
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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: 0.0001 |
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- train_batch_size: 160 |
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- eval_batch_size: 160 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 640 |
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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 | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.2366 | 1.0 | 105 | 0.2193 | 0.9117 | 0.9115 | 0.9139 | 0.9111 | |
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| 0.1104 | 2.0 | 210 | 0.2174 | 0.9243 | 0.9243 | 0.9243 | 0.9243 | |
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| 0.0685 | 2.99 | 315 | 0.2441 | 0.9186 | 0.9185 | 0.9186 | 0.9185 | |
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| 0.0476 | 4.0 | 421 | 0.2524 | 0.9232 | 0.9232 | 0.9233 | 0.9234 | |
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| 0.0319 | 5.0 | 526 | 0.2832 | 0.9220 | 0.9219 | 0.9226 | 0.9217 | |
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| 0.0227 | 6.0 | 631 | 0.3093 | 0.9289 | 0.9289 | 0.9289 | 0.9289 | |
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| 0.0169 | 6.99 | 736 | 0.3755 | 0.9209 | 0.9209 | 0.9208 | 0.9210 | |
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| 0.0112 | 8.0 | 842 | 0.3793 | 0.9220 | 0.9219 | 0.9234 | 0.9215 | |
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| 0.0079 | 9.0 | 947 | 0.3980 | 0.9255 | 0.9254 | 0.9255 | 0.9254 | |
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| 0.007 | 9.98 | 1050 | 0.4216 | 0.9300 | 0.9300 | 0.9302 | 0.9299 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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