Instructions to use DL-Project/hatespeech_ast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DL-Project/hatespeech_ast with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="DL-Project/hatespeech_ast")# Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("DL-Project/hatespeech_ast") model = AutoModelForAudioClassification.from_pretrained("DL-Project/hatespeech_ast") - Notebooks
- Google Colab
- Kaggle
correct eval and test scores
Browse files
README.md
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This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6441
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- Accuracy: 0.6318
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- Recall: 0.8191
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This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6306
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- Accuracy: 0.6486
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- Recall: 0.8368
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- Precision: 0.6136
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- F1: 0.7080
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And the following results on the test set:
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- Loss: 0.6441
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- Accuracy: 0.6318
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- Recall: 0.8191
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