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--- |
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library_name: transformers |
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license: mit |
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base_model: cardiffnlp/twitter-roberta-large-hate-latest |
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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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- f1 |
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model-index: |
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- name: twitter-roberta-large-hate-latest-roman-urdu-fine-grained |
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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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# twitter-roberta-large-hate-latest-roman-urdu-fine-grained |
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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: 0.8408 |
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- Accuracy: 0.8023 |
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- Precision: 0.7245 |
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- Recall: 0.7129 |
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- F1: 0.7177 |
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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: 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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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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| 0.9091 | 1.0 | 113 | 0.8078 | 0.7034 | 0.3788 | 0.4083 | 0.3878 | |
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| 1.1008 | 2.0 | 226 | 0.9036 | 0.6510 | 0.2624 | 0.3397 | 0.2841 | |
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| 0.7624 | 3.0 | 339 | 0.6444 | 0.7844 | 0.7015 | 0.6274 | 0.6544 | |
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| 0.5512 | 4.0 | 452 | 0.5075 | 0.8342 | 0.7675 | 0.7453 | 0.7463 | |
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| 0.5258 | 5.0 | 565 | 0.3519 | 0.8858 | 0.8263 | 0.8081 | 0.8163 | |
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| 0.3489 | 6.0 | 678 | 0.3154 | 0.9011 | 0.8612 | 0.8260 | 0.8399 | |
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| 0.3182 | 7.0 | 791 | 0.2394 | 0.9295 | 0.8985 | 0.8864 | 0.8895 | |
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| 0.2263 | 8.0 | 904 | 0.1722 | 0.9502 | 0.9092 | 0.9223 | 0.9143 | |
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| 0.2024 | 9.0 | 1017 | 0.1252 | 0.9684 | 0.9474 | 0.9441 | 0.9457 | |
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| 0.1757 | 10.0 | 1130 | 0.1101 | 0.9736 | 0.9592 | 0.9506 | 0.9548 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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