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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_suicide
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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_suicide
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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: 0.5994
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- Accuracy: 0.7446
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- F1: 0.7487
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- Precision: 0.7368
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- Recall: 0.7609
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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: 7
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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 | 0.97 | 25 | 0.6934 | 0.5 | 0.0 | 0.0 | 0.0 |
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| 0.691 | 1.98 | 51 | 0.6905 | 0.5 | 0.0213 | 0.5 | 0.0109 |
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| 0.6866 | 2.99 | 77 | 0.6666 | 0.6522 | 0.5493 | 0.78 | 0.4239 |
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| 0.6427 | 4.0 | 103 | 0.5652 | 0.7174 | 0.7011 | 0.7439 | 0.6630 |
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| 0.5594 | 4.97 | 128 | 0.5586 | 0.7228 | 0.6982 | 0.7662 | 0.6413 |
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| 0.521 | 5.98 | 154 | 0.5405 | 0.7283 | 0.7283 | 0.7283 | 0.7283 |
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| 0.4097 | 6.8 | 175 | 0.5994 | 0.7446 | 0.7487 | 0.7368 | 0.7609 |
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### Framework versions
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- Transformers 4.38.2
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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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model.safetensors
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