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README.md
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---
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license: cc-by-nc-4.0
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base_model: mental/mental-roberta-base
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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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- f1
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- precision
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- recall
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model-index:
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- name: mental_roberta_stress
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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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# mental_roberta_stress
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This model is a fine-tuned version of [mental/mental-roberta-base](https://huggingface.co/mental/mental-roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4034
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- Accuracy: 0.8266
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- F1: 0.8278
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- Precision: 0.8490
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- Recall: 0.8076
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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: 4
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 64
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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: 500
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- num_epochs: 4
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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 | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 0.6934 | 0.99 | 31 | 0.6885 | 0.5427 | 0.6930 | 0.5302 | 1.0 |
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| 0.6692 | 1.98 | 62 | 0.6606 | 0.5664 | 0.7042 | 0.5434 | 1.0 |
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| 0.46 | 2.98 | 93 | 0.4357 | 0.8098 | 0.8116 | 0.8300 | 0.7940 |
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| 0.3539 | 3.97 | 124 | 0.4034 | 0.8266 | 0.8278 | 0.8490 | 0.8076 |
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### Framework versions
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- Transformers 4.39.1
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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