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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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datasets: |
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- tweets_hate_speech_detection |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: FirstTry |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: tweets_hate_speech_detection |
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type: tweets_hate_speech_detection |
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config: default |
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split: train |
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args: default |
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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 |
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should probably proofread and complete it, then remove this comment. --> |
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# FirstTry |
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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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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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: 15 |
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### Training results |
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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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- Transformers 4.26.1 |
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- Pytorch 2.0.1 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.3 |
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