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SBERT-am-finetune
heran/SBERT-am-finetune
SBERT-am-finetune is a feature extraction model from heran. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as afl-3.0.
This is a fine-tuned version of Sentence-BERT (SBERT) specifically designed for the Amharic language. It was trained on a Natural Language Inference (NLI) dataset written in the Amharic language. The model outputs sen…
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At a glance
- Task
- Feature Extraction
- Library
- transformers
- License
- afl-3.0
- Model type
- bert
- Access
- Public
- Created
- Jan 10, 2023
- Updated
- Jun 20, 2025
- SHA
- 2dfdc2d3
Model card
From the Hugging Face model README
Model Description
This is a fine-tuned version of Sentence-BERT (SBERT) specifically designed for the Amharic language. It was trained on a Natural Language Inference (NLI) dataset written in the Amharic language. The model outputs sentence embeddings in the form of 768-dimensional vectors.
Usage
This model can be used as input for downstream tasks such as sentiment analysis, recommendation systems, question answering, text summarization, named entity recognition, etc.
from sentence_transformers import SentenceTransformer
SentenceModel = SentenceTransformer('heran/SBERT-am-finetune')
textEncoding = SentenceModel.encode("ዛሬ አየሩ በጣም ጥሩ ነው።")
Limitations and Known Issues
It is important to note that the model was trained on a limited dataset; it may have inherent biases and may not perform optimally for sentences that contain infrequently used words. It is recommended to carefully evaluate the model's output and consider supplementing it with additional training data or methods to mitigate these limitations.
- Task
- Feature Extraction
- Library
- transformers
- Type
- bert
- License
- afl-3.0
- Languages
- am
- Created
- Jan 10, 2023
- Updated
- Jun 20, 2025