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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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datasets:
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- go_emotions
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metrics:
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- f1
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- accuracy
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model-index:
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- name: pretrained_model
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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: go_emotions
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type: go_emotions
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config: simplified
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split: validation
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args: simplified
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metrics:
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- name: F1
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type: f1
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value: 0.572835583796664
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- name: Accuracy
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type: accuracy
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value: 0.45466273497972726
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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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# pretrained_model
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the go_emotions dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1240
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- F1: 0.5728
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- Roc Auc: 0.7737
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- Accuracy: 0.4547
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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: 64
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- eval_batch_size: 64
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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 | F1 | Roc Auc | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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| 0.1205 | 1.0 | 679 | 0.0865 | 0.5632 | 0.7347 | 0.4458 |
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| 0.0859 | 2.0 | 1358 | 0.0829 | 0.5717 | 0.7378 | 0.4521 |
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| 0.0727 | 3.0 | 2037 | 0.0827 | 0.5897 | 0.7523 | 0.4753 |
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| 0.0629 | 4.0 | 2716 | 0.0857 | 0.5808 | 0.7535 | 0.4652 |
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| 0.0568 | 5.0 | 3395 | 0.0904 | 0.5868 | 0.7616 | 0.4821 |
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| 0.0423 | 6.0 | 4074 | 0.0989 | 0.5806 | 0.7682 | 0.4724 |
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| 0.0344 | 7.0 | 4753 | 0.1079 | 0.5736 | 0.7657 | 0.4650 |
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| 0.0296 | 8.0 | 5432 | 0.1158 | 0.5637 | 0.7649 | 0.4504 |
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| 0.0206 | 9.0 | 6111 | 0.1200 | 0.5674 | 0.7689 | 0.4486 |
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| 0.0177 | 10.0 | 6790 | 0.1240 | 0.5728 | 0.7737 | 0.4547 |
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### Framework versions
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- Transformers 4.26.0
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- Pytorch 1.13.1+cu116
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- Datasets 2.9.0
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- Tokenizers 0.13.2
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