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README.md
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---
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base_model: readerbench/RoBERT-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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- precision
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- recall
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model-index:
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- name: ro-offense-01
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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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# ro-offense-01
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This model is a fine-tuned version of [readerbench/RoBERT-base](https://huggingface.co/readerbench/RoBERT-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7285
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- Accuracy: 0.8132
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- Precision: 0.8131
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- Recall: 0.8173
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- F1 Macro: 0.8123
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- F1 Micro: 0.8132
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- F1 Weighted: 0.8094
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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: 64
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- eval_batch_size: 128
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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 | Precision | Recall | F1 Macro | F1 Micro | F1 Weighted |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:--------:|:-----------:|
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| No log | 1.0 | 125 | 0.6284 | 0.7675 | 0.7662 | 0.7721 | 0.7681 | 0.7675 | 0.7654 |
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| No log | 2.0 | 250 | 0.5576 | 0.7820 | 0.7826 | 0.7799 | 0.7796 | 0.7820 | 0.7803 |
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| No log | 3.0 | 375 | 0.5405 | 0.8001 | 0.8122 | 0.8077 | 0.8026 | 0.8001 | 0.7943 |
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| 0.5338 | 4.0 | 500 | 0.5853 | 0.8172 | 0.8140 | 0.8120 | 0.8124 | 0.8172 | 0.8161 |
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| 0.5338 | 5.0 | 625 | 0.6476 | 0.8157 | 0.8143 | 0.8098 | 0.8118 | 0.8157 | 0.8148 |
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| 0.5338 | 6.0 | 750 | 0.6607 | 0.8122 | 0.8137 | 0.8173 | 0.8120 | 0.8122 | 0.8082 |
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| 0.5338 | 7.0 | 875 | 0.7285 | 0.8132 | 0.8131 | 0.8173 | 0.8123 | 0.8132 | 0.8094 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.3
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- Tokenizers 0.13.3
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