Text Classification
Transformers
TensorBoard
Safetensors
Amharic
xlm-roberta
amharic
sentiment-analysis
intent-classification
code-switching
ethiopia
adfluence-ai
Eval Results (legacy)
text-embeddings-inference
Instructions to use YosefA/adfluence-intent-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YosefA/adfluence-intent-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YosefA/adfluence-intent-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YosefA/adfluence-intent-model") model = AutoModelForSequenceClassification.from_pretrained("YosefA/adfluence-intent-model") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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The dataset was created through the following process:
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* **Source:** Started with ~
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* **Transformation:** Each review was programmatically rephrased and translated into a simulated social media comment using Google's Gemini Flash.
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* **Stylization:** Comments were generated in three styles to mimic real-world Ethiopian user behavior:
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* Amharic (Ge’ez script)
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The dataset was created through the following process:
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* **Source:** Started with ~3180 English product reviews from an Amazon dataset.
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* **Transformation:** Each review was programmatically rephrased and translated into a simulated social media comment using Google's Gemini Flash.
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* **Stylization:** Comments were generated in three styles to mimic real-world Ethiopian user behavior:
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* Amharic (Ge’ez script)
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