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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- rexarski/TCFD_disclosure |
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base_model: distilroberta-base |
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model-index: |
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- name: distilroberta-tcfd-disclosure |
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results: [] |
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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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# distilroberta-tcfd-disclosure |
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8681 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 5 |
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- total_train_batch_size: 80 |
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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: 50 |
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- num_epochs: 20 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| No log | 1.0 | 5 | 2.3837 | |
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| 2.3918 | 2.0 | 10 | 2.3787 | |
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| 2.3918 | 3.0 | 15 | 2.3704 | |
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| 2.3754 | 4.0 | 20 | 2.3623 | |
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| 2.3754 | 5.0 | 25 | 2.3396 | |
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| 2.2976 | 6.0 | 30 | 2.2599 | |
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| 2.2976 | 7.0 | 35 | 2.1095 | |
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| 2.0439 | 8.0 | 40 | 2.0184 | |
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| 2.0439 | 9.0 | 45 | 1.9059 | |
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| 1.6799 | 10.0 | 50 | 1.8469 | |
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| 1.6799 | 11.0 | 55 | 1.8089 | |
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| 1.2948 | 12.0 | 60 | 1.7263 | |
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| 1.2948 | 13.0 | 65 | 1.7250 | |
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| 0.9621 | 14.0 | 70 | 1.8106 | |
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| 0.9621 | 15.0 | 75 | 1.8073 | |
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| 0.7356 | 16.0 | 80 | 1.8681 | |
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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 2.12.0 |
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- Tokenizers 0.13.3 |
