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--- |
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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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model-index: |
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- name: albert-base-v2-finetuned-rte |
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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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args: rte |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7545126353790613 |
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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-base-v2-finetuned-rte |
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3395 |
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- Accuracy: 0.7545 |
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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: 10 |
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- eval_batch_size: 10 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 249 | 0.6305 | 0.6859 | |
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| No log | 2.0 | 498 | 0.6054 | 0.7040 | |
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| 0.5964 | 3.0 | 747 | 0.7142 | 0.7437 | |
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| 0.5964 | 4.0 | 996 | 1.2917 | 0.7437 | |
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| 0.2546 | 5.0 | 1245 | 1.3395 | 0.7545 | |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.0 |
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- Tokenizers 0.10.3 |
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