Instructions to use anilguven/albert_tr_qa_turkish_squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anilguven/albert_tr_qa_turkish_squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="anilguven/albert_tr_qa_turkish_squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("anilguven/albert_tr_qa_turkish_squad") model = AutoModelForQuestionAnswering.from_pretrained("anilguven/albert_tr_qa_turkish_squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| language: | |
| - tr | |
| metrics: | |
| - accuracy | |
| - f1 | |
| pipeline_tag: question-answering | |
| tags: | |
| - qa | |
| - question answering | |
| - squad | |
| - albert | |
| ### Information | |
| This model was developed/finetuned for QA task for the Turkish Language. This model was finetuned via [Turkish SQuAD](https://github.com/boun-tabi/SQuAD-TR) dataset. | |
| ### Model Sources | |
| - **Dataset:** https://github.com/boun-tabi/SQuAD-TR | |
| - **Paper:** peer review (Springer) | |
| - **Finetuned from model:** https://huggingface.co/loodos/albert-base-turkish-uncased | |
| ### Results | |
| - Accuracy: %71.58 | |
| ### Citation | |
| BibTeX: | |
| Peer review process |