Instructions to use heran/SBERT-am-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heran/SBERT-am-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="heran/SBERT-am-finetune")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("heran/SBERT-am-finetune") model = AutoModel.from_pretrained("heran/SBERT-am-finetune") - Notebooks
- Google Colab
- Kaggle
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.
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