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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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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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model-index:
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- name: albert-large-v2-finetuned-mrpc
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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: mrpc
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split: validation
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args: mrpc
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8872549019607843
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- name: F1
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type: f1
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value: 0.9190140845070423
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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-large-v2-finetuned-mrpc
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This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4449
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- Accuracy: 0.8873
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- F1: 0.9190
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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: 32
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- eval_batch_size: 32
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- seed: 71
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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 | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| No log | 1.0 | 115 | 0.3615 | 0.8456 | 0.8844 |
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| No log | 2.0 | 230 | 0.2958 | 0.8824 | 0.9155 |
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| No log | 3.0 | 345 | 0.3497 | 0.8701 | 0.9081 |
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| No log | 4.0 | 460 | 0.4280 | 0.8725 | 0.9100 |
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| 0.2707 | 5.0 | 575 | 0.4449 | 0.8873 | 0.9190 |
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### Framework versions
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- Transformers 4.28.0
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- Pytorch 2.1.0+cu118
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- Datasets 2.15.0
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- Tokenizers 0.13.3
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