Question Answering
Transformers
Safetensors
Turkish
mt5
text2text-generation
text-generation
turkish
squad
Instructions to use anilguven/mt5-small_squad_tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anilguven/mt5-small_squad_tr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="anilguven/mt5-small_squad_tr")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("anilguven/mt5-small_squad_tr") model = AutoModelForSeq2SeqLM.from_pretrained("anilguven/mt5-small_squad_tr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# mT5-small based Turkish Question Answering System
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[Google's Multilingual T5-small](https://huggingface.co/google/mt5-small) is fine-tuned on [Turkish SQuAD](https://
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The notebook that includes all fine tuning process will be shared on my Github page later [github](https://github.com/google-research/multilingual-t5). mT5 small model has 300 million parameters and model size is about 1.2GB. Therefore, it takes significant amount of time to fine tune it.
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# mT5-small based Turkish Question Answering System
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[Google's Multilingual T5-small](https://huggingface.co/google/mt5-small) is fine-tuned on [Turkish SQuAD](https://github.com/boun-tabi/SQuAD-TR) for **Q&A** downstream task by using Pytorch Lightning.
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The notebook that includes all fine tuning process will be shared on my Github page later [github](https://github.com/google-research/multilingual-t5). mT5 small model has 300 million parameters and model size is about 1.2GB. Therefore, it takes significant amount of time to fine tune it.
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