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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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model-index:
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- name: mlcovid19-classifier
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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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# mlcovid19-classifier
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4116
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- F1 Macro: 0.6750
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- F1 Misinformation: 0.9407
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- F1 Factual: 0.8529
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- F1 Other: 0.2315
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- Prec Macro: 0.7057
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- Prec Misinformation: 0.9229
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- Prec Factual: 0.8958
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- Prec Other: 0.2983
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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: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 256
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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: 4367
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- num_epochs: 30
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Misinformation | F1 Factual | F1 Other | Prec Macro | Prec Misinformation | Prec Factual | Prec Other |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:----------:|:--------:|:----------:|:-------------------:|:------------:|:----------:|
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| 0.8111 | 3.67 | 500 | 0.4101 | 0.5506 | 0.9162 | 0.7356 | 0.0 | 0.5421 | 0.8969 | 0.7295 | 0.0 |
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| 0.3688 | 7.35 | 1000 | 0.3397 | 0.5770 | 0.9321 | 0.7988 | 0.0 | 0.5694 | 0.9111 | 0.7972 | 0.0 |
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| 0.3012 | 11.03 | 1500 | 0.3011 | 0.5912 | 0.9415 | 0.8322 | 0.0 | 0.5955 | 0.9104 | 0.8761 | 0.0 |
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| 0.249 | 14.7 | 2000 | 0.3020 | 0.5931 | 0.9404 | 0.8388 | 0.0 | 0.5841 | 0.9206 | 0.8317 | 0.0 |
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| 0.1957 | 18.38 | 2500 | 0.3308 | 0.6402 | 0.9406 | 0.8433 | 0.1365 | 0.7126 | 0.9234 | 0.8445 | 0.3699 |
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| 0.1438 | 22.06 | 3000 | 0.3502 | 0.6615 | 0.9406 | 0.8529 | 0.1911 | 0.6952 | 0.9283 | 0.8543 | 0.3030 |
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| 0.0996 | 25.73 | 3500 | 0.4116 | 0.6750 | 0.9407 | 0.8529 | 0.2315 | 0.7057 | 0.9229 | 0.8958 | 0.2983 |
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| 0.0657 | 29.41 | 4000 | 0.4413 | 0.6422 | 0.9428 | 0.8497 | 0.1342 | 0.7126 | 0.9269 | 0.8453 | 0.3655 |
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### Framework versions
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- Transformers 4.23.0
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- Pytorch 1.12.1+cu113
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- Datasets 2.5.2
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- Tokenizers 0.13.1
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