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--- |
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language: |
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- en |
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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: baseline-ft-mrpc-IRoberta-b-unquantized |
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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 MRPC |
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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: 0.8995098039215687 |
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- name: F1 |
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type: f1 |
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value: 0.9266547406082289 |
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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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# baseline-ft-mrpc-IRoberta-b-unquantized |
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This model is a fine-tuned version of [kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base) on the GLUE MRPC dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5354 |
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- Accuracy: 0.8995 |
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- F1: 0.9267 |
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- Combined Score: 0.9131 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 5.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| |
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| 0.1212 | 1.0 | 230 | 0.3401 | 0.8799 | 0.9136 | 0.8967 | |
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| 0.0347 | 2.0 | 460 | 0.3085 | 0.8676 | 0.9059 | 0.8868 | |
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| 0.0495 | 3.0 | 690 | 0.3552 | 0.8848 | 0.9174 | 0.9011 | |
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| 0.0024 | 4.0 | 920 | 0.4960 | 0.8824 | 0.9158 | 0.8991 | |
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| 0.0046 | 5.0 | 1150 | 0.5354 | 0.8995 | 0.9267 | 0.9131 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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