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README.md
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---
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license: mit
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: ACTS_feedback1
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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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# ACTS_feedback1
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2357
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- Accuracy: 0.8936
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- Balanced accuracy: 0.8897
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- Precision: 0.8951
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- Recall: 0.8936
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- F1: 0.8915
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:---------:|:------:|:------:|
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| 1.0881 | 1.0 | 12 | 1.0513 | 0.5532 | 0.5119 | 0.4004 | 0.5532 | 0.4645 |
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| 0.9933 | 2.0 | 24 | 0.9257 | 0.5319 | 0.4952 | 0.3852 | 0.5319 | 0.4463 |
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| 0.8065 | 3.0 | 36 | 0.7059 | 0.7234 | 0.7295 | 0.7607 | 0.7234 | 0.7184 |
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| 0.5504 | 4.0 | 48 | 0.4259 | 0.8511 | 0.8474 | 0.8486 | 0.8511 | 0.8472 |
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| 0.3262 | 5.0 | 60 | 0.3703 | 0.8511 | 0.8654 | 0.8624 | 0.8511 | 0.8499 |
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| 0.1877 | 6.0 | 72 | 0.2518 | 0.8723 | 0.8731 | 0.8719 | 0.8723 | 0.8703 |
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| 0.1094 | 7.0 | 84 | 0.2283 | 0.9362 | 0.9410 | 0.9415 | 0.9362 | 0.9365 |
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| 0.0721 | 8.0 | 96 | 0.2246 | 0.9149 | 0.9244 | 0.9233 | 0.9149 | 0.9149 |
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| 0.0521 | 9.0 | 108 | 0.2215 | 0.8936 | 0.8897 | 0.8951 | 0.8936 | 0.8915 |
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| 0.0455 | 10.0 | 120 | 0.2357 | 0.8936 | 0.8897 | 0.8951 | 0.8936 | 0.8915 |
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### Framework versions
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- Transformers 4.18.0
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- Pytorch 1.10.0+cu111
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- Datasets 2.0.0
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- Tokenizers 0.11.6
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