Instructions to use ruanchaves/mdeberta-v3-base-hatebr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ruanchaves/mdeberta-v3-base-hatebr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ruanchaves/mdeberta-v3-base-hatebr")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ruanchaves/mdeberta-v3-base-hatebr") model = AutoModelForSequenceClassification.from_pretrained("ruanchaves/mdeberta-v3-base-hatebr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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print(f"{i+1}) Label: {l} Score: {np.round(float(s), 4)}")
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```
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## Citation
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Our research is ongoing, and we are currently working on describing our experiments in a paper, which will be published soon.
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print(f"{i+1}) Label: {l} Score: {np.round(float(s), 4)}")
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```
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## Licensing Information
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The HateBR dataset, including all its components, is provided strictly for academic and research purposes. The use of the dataset for any commercial or non-academic purpose is expressly prohibited without the prior written consent of [SINCH](https://www.sinch.com/).
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## Citation
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Our research is ongoing, and we are currently working on describing our experiments in a paper, which will be published soon.
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