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mlcovid19-classifier

Text Classification
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xlm-roberta
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@@ -11,19 +11,18 @@ model-index:
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  should probably proofread and complete it, then remove this comment. -->
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  # mlcovid19-classifier
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- - [Mulit-lingual COVID-19 Fake News Detection and Intervention](https://counterinfodemic.org/)
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- This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on Multi-lingual COVID19 Fake News dataset. Please visite our project [website](https://counterinfodemic.org/) for more info.
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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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@@ -51,25 +50,22 @@ The following hyperparameters were used during training:
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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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  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 [oscarwu/mlcovid19-classifier](https://huggingface.co/oscarwu/mlcovid19-classifier) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5651
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+ - F1 Macro: 0.6566
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+ - F1 Misinformation: 0.9336
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+ - F1 Factual: 0.8316
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+ - F1 Other: 0.2048
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+ - Prec Macro: 0.6775
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+ - Prec Misinformation: 0.9344
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+ - Prec Factual: 0.7907
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+ - Prec Other: 0.3075
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  ## Model description
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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: 60
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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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+ | 0.5055 | 3.67 | 500 | 0.3267 | 0.6006 | 0.9440 | 0.8517 | 0.0062 | 0.8132 | 0.9228 | 0.8502 | 0.6667 |
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+ | 0.0876 | 7.35 | 1000 | 0.3922 | 0.6636 | 0.9412 | 0.8533 | 0.1963 | 0.6975 | 0.9255 | 0.8729 | 0.2941 |
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+ | 0.0477 | 11.03 | 1500 | 0.4479 | 0.6715 | 0.9404 | 0.8562 | 0.2178 | 0.6939 | 0.9288 | 0.8695 | 0.2836 |
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+ | 0.0334 | 14.7 | 2000 | 0.5123 | 0.6622 | 0.9418 | 0.8515 | 0.1935 | 0.6996 | 0.9251 | 0.8732 | 0.3007 |
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+ | 0.0271 | 18.38 | 2500 | 0.5651 | 0.6566 | 0.9336 | 0.8316 | 0.2048 | 0.6775 | 0.9344 | 0.7907 | 0.3075 |
 
 
 
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  ### Framework versions
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+ - Transformers 4.23.1
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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