README.md

---
license: apache-2.0
base_model: google/mt5-base
tags:
- Question Answering
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mT5-base-turkish-qa
  results: []
language:
- tr
pipeline_tag: text2text-generation
widget:
- text: >-
    Soru: Nazım Hikmet ne zaman doğmuştur?

    Metin: 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.
datasets:
- ucsahin/TR-Extractive-QA-82K
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# mT5-base-turkish-qa

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the [ucsahin/TR-Extractive-QA-82K](https://huggingface.co/datasets/ucsahin/TR-Extractive-QA-82K) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5109
- Rouge1: 79.3283
- Rouge2: 68.0845
- Rougel: 79.3474
- Rougelsum: 79.2937

## Model description

mT5-base model is trained with manually curated Turkish dataset consisting of 65K training samples with ("question", "answer", "context") triplets.  

## Intended uses & limitations

The intended use of the model is extractive question answering.

In order to use the inference widget, enter your input in the format:
```
Soru: question_text
Metin: context_text
```

Generated response by the model:
```
Cevap: answer_text
```

Use with Transformers:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from datasets import load_dataset

# Load the dataset
qa_tr_datasets = load_dataset("ucsahin/TR-Extractive-QA-82K")

# Load model and tokenizer
model_checkpoint = "ucsahin/mT5-base-turkish-qa"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)

inference_dataset = qa_tr_datasets["test"].select(range(10))

for input in inference_dataset:
    input_question = "Soru: " + input["question"]
    input_context = "Metin: " + input["context"]

    tokenized_inputs = tokenizer(input_question, input_context, max_length=512, truncation=True, return_tensors="pt")
    outputs = model.generate(input_ids=tokenized_inputs["input_ids"], max_new_tokens=32)
    output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)

    print(f"Reference answer: {input['answer']}, Model Answer: {output_text}")
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.0454        | 0.13  | 500  | 0.6771          | 73.1040 | 59.8915 | 73.1819 | 73.0558   |
| 0.8012        | 0.26  | 1000 | 0.6012          | 76.3357 | 64.1967 | 76.3796 | 76.2688   |
| 0.7703        | 0.39  | 1500 | 0.5844          | 76.8932 | 65.2509 | 76.9932 | 76.9418   |
| 0.6783        | 0.51  | 2000 | 0.5587          | 76.7284 | 64.8453 | 76.7416 | 76.6720   |
| 0.6546        | 0.64  | 2500 | 0.5362          | 78.2261 | 66.5893 | 78.2515 | 78.2142   |
| 0.6289        | 0.77  | 3000 | 0.5133          | 78.6917 | 67.1534 | 78.6852 | 78.6319   |
| 0.6292        | 0.9   | 3500 | 0.5109          | 79.3283 | 68.0845 | 79.3474 | 79.2937   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0