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--- |
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license: mit |
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base_model: uhhlt/am-roberta |
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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: am-roberta-finetuned |
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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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# amharic-hate-speech |
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This model is a fine-tuned version of [uhhlt/am-roberta](https://huggingface.co/uhhlt/am-roberta) on an [AmahricHateSpeechRANL](https://huggingface.co/datasets/uhhlt/amharichatespeechranlp) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6437 |
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- Accuracy: 0.7373 |
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- Precision: 0.7216 |
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- Recall: 0.7149 |
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- F1: 0.7180 |
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## How to use it |
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``` python |
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from transformers import pipeline |
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amhate_classifier = pipeline("text-classification", model="uhhlt/amharic-hate-speech") |
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amhate_classifier(["π³βοΈ π³βοΈααα
αα
α¨αα°ααα ααα α°α»ααͺ α’αα¨α΅α΅ααα΅ !!!π³βοΈ π³βοΈα’", |
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"α αα° α αα αα α¨ααα
αα
? αα‘α α αααͺ α¨ααα α«α΅ααα’ α°αα α«αα αα‘α α₯α¨αα° ααα ααα α«αα αααα α€αααα΅ α ααα΅αα α’ αα 100% α«αΈααα", |
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"α α αα΅α
α°α°α¨α α£αα³ α°αααͺ"]) |
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``` |
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Output |
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``` |
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[{'label': 'normal', 'score': 0.8840981721878052}, |
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{'label': 'hate', 'score': 0.519339382648468}, |
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{'label': 'hate', 'score': 0.9630571007728577}] |
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``` |
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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: 128 |
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- eval_batch_size: 128 |
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- seed: 16 |
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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: 8 |
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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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| 0.8441 | 1.0 | 94 | 0.6699 | 0.7053 | 0.6913 | 0.6640 | 0.6737 | |
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| 0.6199 | 2.0 | 188 | 0.6505 | 0.72 | 0.7060 | 0.6995 | 0.6994 | |
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| 0.5295 | 3.0 | 282 | 0.6240 | 0.736 | 0.7201 | 0.7125 | 0.7159 | |
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| 0.4614 | 4.0 | 376 | 0.6437 | 0.7373 | 0.7216 | 0.7149 | 0.7180 | |
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| 0.3955 | 5.0 | 470 | 0.6922 | 0.7207 | 0.7001 | 0.7072 | 0.7031 | |
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| 0.3529 | 6.0 | 564 | 0.6995 | 0.7247 | 0.7050 | 0.7029 | 0.7039 | |
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| 0.3076 | 7.0 | 658 | 0.7352 | 0.7253 | 0.7067 | 0.7000 | 0.7031 | |
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| 0.2863 | 8.0 | 752 | 0.7470 | 0.7227 | 0.7019 | 0.6983 | 0.7000 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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