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callmesan
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twitter-roberta-large-hate-latest-roman-urdu-binary

Text Classification
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roberta
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  1. README.md +17 -12
  2. model.safetensors +1 -1
README.md CHANGED
@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [cardiffnlp/twitter-roberta-large-hate-latest](https://huggingface.co/cardiffnlp/twitter-roberta-large-hate-latest) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3062
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- - Accuracy: 0.9021
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- - Precision: 0.9014
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- - Recall: 0.9028
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- - F1: 0.9019
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  ## Model description
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@@ -44,7 +44,7 @@ More information needed
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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: 128
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  - seed: 42
@@ -52,17 +52,22 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 128
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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: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 0.4413 | 0.9912 | 56 | 0.3491 | 0.8489 | 0.8505 | 0.8461 | 0.8474 |
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- | 0.2844 | 2.0 | 113 | 0.3017 | 0.8677 | 0.8697 | 0.8648 | 0.8663 |
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- | 0.2437 | 2.9912 | 169 | 0.3157 | 0.8739 | 0.8731 | 0.8739 | 0.8735 |
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- | 0.1735 | 4.0 | 226 | 0.3252 | 0.8752 | 0.8759 | 0.8732 | 0.8742 |
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- | 0.1438 | 4.9558 | 280 | 0.3455 | 0.8777 | 0.8769 | 0.8778 | 0.8772 |
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [cardiffnlp/twitter-roberta-large-hate-latest](https://huggingface.co/cardiffnlp/twitter-roberta-large-hate-latest) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4537
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+ - Accuracy: 0.9016
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+ - Precision: 0.9015
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+ - Recall: 0.9007
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+ - F1: 0.9011
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  ## Model description
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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: 32
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  - eval_batch_size: 128
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  - seed: 42
 
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  - total_train_batch_size: 128
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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 |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.4956 | 0.9912 | 56 | 0.4878 | 0.8215 | 0.8208 | 0.8202 | 0.8205 |
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+ | 0.3268 | 2.0 | 113 | 0.3194 | 0.8652 | 0.8699 | 0.8611 | 0.8632 |
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+ | 0.2555 | 2.9912 | 169 | 0.3241 | 0.8839 | 0.8894 | 0.8798 | 0.8822 |
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+ | 0.1832 | 4.0 | 226 | 0.3184 | 0.8914 | 0.8930 | 0.8891 | 0.8904 |
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+ | 0.1283 | 4.9912 | 282 | 0.3375 | 0.8976 | 0.9028 | 0.8939 | 0.8962 |
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+ | 0.0815 | 6.0 | 339 | 0.3645 | 0.8939 | 0.8937 | 0.8955 | 0.8937 |
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+ | 0.0961 | 6.9912 | 395 | 0.3691 | 0.9001 | 0.9004 | 0.8988 | 0.8995 |
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+ | 0.0544 | 8.0 | 452 | 0.4781 | 0.8901 | 0.8895 | 0.8910 | 0.8899 |
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+ | 0.0404 | 8.9912 | 508 | 0.4209 | 0.9089 | 0.9104 | 0.9068 | 0.9081 |
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+ | 0.0359 | 9.9115 | 560 | 0.4259 | 0.9051 | 0.9051 | 0.9042 | 0.9046 |
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  ### Framework versions
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