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README.md
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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- F1: 0.
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## Model description
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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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- lr_scheduler_warmup_steps: 5
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- num_epochs:
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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 | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 2.
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| 1.018 | 8.24 | 700 | 0.9516 | 0.4273 |
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| 0.9416 | 9.41 | 800 | 0.8972 | 0.4508 |
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### Framework versions
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.8762
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- F1: 0.1946
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## Model description
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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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- lr_scheduler_warmup_steps: 5
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- num_epochs: 12
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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 | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 2.1002 | 1.18 | 100 | 1.9982 | 0.1023 |
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| 1.7832 | 2.35 | 200 | 1.8435 | 0.1310 |
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| 1.57 | 3.53 | 300 | 1.8097 | 0.1552 |
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| 1.3719 | 4.71 | 400 | 1.8216 | 0.1631 |
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| 1.2072 | 5.88 | 500 | 1.8138 | 0.1811 |
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| 1.0186 | 7.06 | 600 | 1.8762 | 0.1946 |
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### Framework versions
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