Text Classification
Transformers
PyTorch
Amharic
bert
Sentiment-Analysis
Hate-Speech
Finetuning-mBERT
text-embeddings-inference
Instructions to use Abel-Mek/amharic_hate_speech_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abel-Mek/amharic_hate_speech_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Abel-Mek/amharic_hate_speech_detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Abel-Mek/amharic_hate_speech_detection") model = AutoModelForSequenceClassification.from_pretrained("Abel-Mek/amharic_hate_speech_detection") - Notebooks
- Google Colab
- Kaggle
Hate-Speech-Detection-in-Amharic-Language-mBERT
This Hugging Face model card contains a machine learning model that uses fine-tuned mBERT to detect hate speech in Amharic language. The model was fine-tuned using the Hugging Face Trainer API.
Fine-Tuning
This model was created by finetuning the mBERT model for the downstream task of Hate speech detection for the Amharic language. The initial mBERT model used for finetuning is http://Davlan/bert-base-multilingual-cased-finetuned-amharic which was provided by Davlan on Huggingface.
Usage
You can use the model through the Hugging Face Transformers library, either by directly loading the model in your Python code or by using the Hugging Face model hub.
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