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update model card README.md

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@@ -22,10 +22,10 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9759098967567004
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  - name: F1
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  type: f1
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- value: 0.8034042553191489
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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
@@ -35,9 +35,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweets_hate_speech_detection dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0977
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- - Accuracy: 0.9759
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- - F1: 0.8034
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  ## Model description
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@@ -68,29 +68,12 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | No log | 0.04 | 50 | 0.2125 | 0.9337 | 0.0 |
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- | No log | 0.07 | 100 | 0.2210 | 0.9341 | 0.0125 |
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- | No log | 0.11 | 150 | 0.1832 | 0.9554 | 0.5103 |
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- | No log | 0.14 | 200 | 0.1539 | 0.9583 | 0.6377 |
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- | No log | 0.18 | 250 | 0.2435 | 0.9523 | 0.4434 |
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- | No log | 0.21 | 300 | 0.1818 | 0.9589 | 0.5736 |
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- | No log | 0.25 | 350 | 0.1138 | 0.9618 | 0.7136 |
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- | No log | 0.29 | 400 | 0.1045 | 0.9667 | 0.7243 |
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- | No log | 0.32 | 450 | 0.0958 | 0.9676 | 0.7330 |
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- | 0.1788 | 0.36 | 500 | 0.0935 | 0.9695 | 0.7306 |
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- | 0.1788 | 0.39 | 550 | 0.1289 | 0.9666 | 0.7178 |
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- | 0.1788 | 0.43 | 600 | 0.1039 | 0.9648 | 0.7507 |
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- | 0.1788 | 0.46 | 650 | 0.1234 | 0.9646 | 0.6435 |
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- | 0.1788 | 0.5 | 700 | 0.0984 | 0.9703 | 0.7725 |
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- | 0.1788 | 0.54 | 750 | 0.1364 | 0.9702 | 0.7185 |
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- | 0.1788 | 0.57 | 800 | 0.1004 | 0.9739 | 0.7792 |
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- | 0.1788 | 0.61 | 850 | 0.0998 | 0.9684 | 0.7616 |
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- | 0.1788 | 0.64 | 900 | 0.1068 | 0.9738 | 0.7857 |
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- | 0.1788 | 0.68 | 950 | 0.1206 | 0.9732 | 0.7644 |
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- | 0.1198 | 0.71 | 1000 | 0.0977 | 0.9759 | 0.8034 |
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- | 0.1198 | 0.75 | 1050 | 0.0864 | 0.9742 | 0.7916 |
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- | 0.1198 | 0.79 | 1100 | 0.1297 | 0.9727 | 0.7849 |
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- | 0.1198 | 0.82 | 1150 | 0.0969 | 0.9751 | 0.8026 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9821679962458939
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  - name: F1
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  type: f1
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+ value: 0.8692660550458716
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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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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweets_hate_speech_detection dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0847
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+ - Accuracy: 0.9822
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+ - F1: 0.8693
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 0.1159 | 1.0 | 1599 | 0.1019 | 0.9759 | 0.8270 |
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+ | 0.0727 | 2.0 | 3198 | 0.0965 | 0.9795 | 0.8424 |
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+ | 0.044 | 3.0 | 4797 | 0.0847 | 0.9822 | 0.8693 |
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+ | 0.0301 | 4.0 | 6396 | 0.1121 | 0.9811 | 0.8660 |
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+ | 0.0206 | 5.0 | 7995 | 0.1718 | 0.9700 | 0.8110 |
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+ | 0.0176 | 6.0 | 9594 | 0.1453 | 0.9811 | 0.8591 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions