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--- |
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license: apache-2.0 |
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base_model: google/mt5-base |
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tags: |
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- Question Answering |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: mT5-base-turkish-qa |
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results: [] |
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language: |
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- tr |
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pipeline_tag: text2text-generation |
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widget: |
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- text: "Soru: Nazım Hikmet ne zaman doğmuştur?\nMetin: Nâzım Hikmet, Mehmed Nâzım adıyla 15 Ocak 1902 tarihinde Selanik'te doğdu. O sırada Hariciye Nezareti memuru olarak Selanik'te çalışan Hikmet Bey, Nâzım'ın çocukluğunda memuriyetten ayrıldı ve ailesiyle birlikte, Halep'te bulunan babasının yanına gitti. Burada bulundukları sırada Hikmet-Celile çiftinin biri Ali İbrahim, diğeri Samiye adında iki çocuğu oldu, fakat Ali İbrahim dizanteriye yakalanıp öldü." |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mT5-base-turkish-qa |
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5109 |
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- Rouge1: 79.3283 |
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- Rouge2: 68.0845 |
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- Rougel: 79.3474 |
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- Rougelsum: 79.2937 |
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## Model description |
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mT5-base model is trained with manually curated Turkish dataset consisting of 65K training samples with ("question", "answer", "context") triplets. |
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## Intended uses & limitations |
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The intended use of the model is extractive question answering. |
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In order to use the inference widget, enter your input in the format: |
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""" |
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Soru: question_text |
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Metin: context_text |
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""" |
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Generated response by the model: |
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""" |
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Cevap: answer_text |
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""" |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
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| 2.0454 | 0.13 | 500 | 0.6771 | 73.1040 | 59.8915 | 73.1819 | 73.0558 | |
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| 0.8012 | 0.26 | 1000 | 0.6012 | 76.3357 | 64.1967 | 76.3796 | 76.2688 | |
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| 0.7703 | 0.39 | 1500 | 0.5844 | 76.8932 | 65.2509 | 76.9932 | 76.9418 | |
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| 0.6783 | 0.51 | 2000 | 0.5587 | 76.7284 | 64.8453 | 76.7416 | 76.6720 | |
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| 0.6546 | 0.64 | 2500 | 0.5362 | 78.2261 | 66.5893 | 78.2515 | 78.2142 | |
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| 0.6289 | 0.77 | 3000 | 0.5133 | 78.6917 | 67.1534 | 78.6852 | 78.6319 | |
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| 0.6292 | 0.9 | 3500 | 0.5109 | 79.3283 | 68.0845 | 79.3474 | 79.2937 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
