the first commit

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README.md

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+---
+language:
+- tr
+arXiv: 2403.01308
+library_name: transformers
+pipeline_tag: text2text-generation
+widget:
+- text: >-
+    Soru yarat: cevap: Alan Mathison Turing İngiliz matematikçi, bilgisayar
+    bilimcisi ve kriptolog. II. Dünya Savaşı sırasında Alman şifrelerinin
+    kırılmasında çok önemli bir rol oynadığı için savaş kahramanı sayılmıştır.
+    Ayrıca Manchester Üniversitesi'nde çalıştığı yıllarda, Turing makinesi
+    denilen algoritma tanımı ile modern bilgisayarların kavramsal temelini
+    atmıştır.
+  example_title: Question generation
+- text: >-
+    Soru cevapla: Turing makinesi denilen algoritma tanımı ile modern
+    bilgisayarların kavramsal temelini atan bilim insanı kimdir? kaynak: Alan
+    Mathison Turing İngiliz matematikçi, bilgisayar bilimcisi ve kriptolog. II.
+    Dünya Savaşı sırasında Alman şifrelerinin kırılmasında çok önemli bir rol
+    oynadığı için savaş kahramanı sayılmıştır. Ayrıca Manchester
+    Üniversitesi'nde çalıştığı yıllarda, Turing makinesi denilen algoritma
+    tanımı ile modern bilgisayarların kavramsal temelini atmıştır.
+  example_title: Question answering
+- text: >-
+    yanıtları çıkar: Alan Mathison Turing İngiliz matematikçi, bilgisayar
+    bilimcisi ve kriptolog.  II. Dünya Savaşı sırasında Alman şifrelerinin
+    kırılmasında çok önemli bir rol oynadığı için savaş kahramanı sayılmıştır.
+    <hl>  Ayrıca Manchester Üniversitesi'nde çalıştığı yıllarda, Turing makinesi
+    denilen algoritma tanımı ile modern bilgisayarların kavramsal temelini
+    atmıştır <hl> .
+  example_title: Answer Extraction
+license: cc-by-nc-sa-4.0
+---
+# VBART Model Card
+
+## Model Description  
+
+This repo contains pretrained tensorflow and safetensors weights of VBART the first sequence-to-sequence model trained in Turkish corpora from scratch. VBART was trained by VNGRS in February 2023.  
+The model is capable of text transformation tasks such as summarization, paraphrasing, and title generation with fine-tuning.
+
+This model overperforms its multilingual counterparts, albeit being much smaller than other implementations.
+
+This repository contains fine-tuned weights of VBART for question-answering and generation tasks described in the [paper](https://doi.org/10.55730/1300-0632.3914).
+
+- **Developed by:** [VNGRS-AI](https://vngrs.com/ai/)
+- **Model type:**  Transformer encoder-decoder based on mBART architecture
+- **Language(s) (NLP):** Turkish
+- **License:** CC BY-NC-SA 4.0
+- **Finetuned from:** VBART-Large
+- **Paper:** [arXiv](https://arxiv.org/abs/2403.01308)
+## How to Get Started with the Model  
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-Large-QAQG",
+                            model_input_names=['input_ids', 'attention_mask'])
+# Uncomment the device_map kwarg and delete the closing bracket to infer model in gpu
+model = AutoModelForSeq2SeqLM.from_pretrained("vngrs-ai/VBART-Large-QAQG")#, device_map="auto")
+
+context="..."
+question="..."
+highlighted_context="..."
+
+# Prompt for question generation
+qg_prompt = f'Soru yarat: cevap: {context}'
+# Prompt for question answering
+qa_prompt = f'Soru cevapla: {question} kaynak: {context}'
+# Prompt for answer extraction
+ae_prompt = f'yanıtları çıkar: {highlighted_context}'
+
+
+# text_input = f"{qg_prompt} {context} "
+token_input = tokenizer(ae_prompt, return_tensors="pt")#.to('cuda')
+
+# token_input
+outputs = model.generate(**token_input)
+print(tokenizer.decode(outputs[0]))
+```
+  
+## Training Details  
+### Fine-tuning prompt
+This model is trained on three tasks: 
+- question answering: Answer a question with given context. Prompted with
+    ```Soru cevapla: <question> kaynak: <context>```
+- question generation: Generate a question from a given context. Will accept a highlight token (`<hl>`, without spaces) to specify the answer to the question generated. Prompted with
+    ```Soru yarat: <context>```
+- answer extraction: Will extract possible answers from a highlighted range (using the same highlight token). Prompted with
+    ``` yanıtları çıkar: <context with highlighted parts>```
+
+### Training Data  
+The base model is pre-trained on cleaned and filtered versions of a mixed corpus made of Turkish parts of [OSCAR-2201](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201) and [mC4](https://huggingface.co/datasets/mc4) datasets. These datasets consist of documents of unstructured web crawl data. More information about the dataset can be found on their respective pages. Data is filtered using a set of heuristics and certain rules, explained in the appendix of our [paper](https://arxiv.org/abs/2403.01308).
+
+The fine-tuning dataset is [TQuAD](https://github.com/obss/turkish-question-generation), which has two versions. We have concatenated them and dropped duplicate samples. More information about this process can be found in Appendix B of our [paper](https://arxiv.org/abs/2403.01308). 
+
+### Limitations
+This model is fine-tuned for question-answering and question-generation tasks with specific prompts. It is not intended to be used in any other case and can not be fine-tuned to any other task with full performance of the base model. It is also not guaranteed that this model will work without specified prompts.
+
+### Training Procedure  
+Pretrained for 30 days and for a total of 708B tokens. Finetuned for 5 epoch.
+#### Hardware
+- **GPUs**: 8 x Nvidia A100-80 GB
+#### Software
+- Tensorflow
+#### Hyperparameters  
+##### Pretraining
+- **Training regime:** fp16 mixed precision
+- **Training objective**: Sentence permutation and span masking (using mask lengths sampled from Poisson distribution λ=3.5, masking 30% of tokens)
+- **Optimizer** : Adam optimizer (β1 = 0.9, β2 = 0.98, Ɛ = 1e-6)
+- **Scheduler**:  Linear decay scheduler (20,000 warm-up steps)
+- **Dropout**: 0.1 (dropped to 0.05 and then to 0 in the last 165k and 205 steps, respectively)
+- **Initial Learning rate**: 5e-6
+- **Training tokens**: 708B
+
+##### Fine-tuning
+- **Training regime:** fp16 mixed precision
+- **Optimizer** : Adam optimizer (β1 = 0.9, β2 = 0.98, Ɛ = 1e-6)
+- **Scheduler**:  Linear decay scheduler
+- **Dropout**: 0.1 
+-  **Learning rate**: 5e-5
+-  **Fine-tune epochs**: 5
+
+#### Metrics
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f8b3c84588fe31f435a92b/D-Epasj5C4icAu0ykqt10.png)
+
+## Citation  
+```
+@article{turker2024vbart,
+  title={VBART: The Turkish LLM},
+  author={Turker, Meliksah and Ari, Erdi and Han, Aydin},
+  journal={arXiv preprint arXiv:2403.01308},
+  year={2024}
+}
+```

config.json

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+{
+  "_name_or_path": "tfhf_model",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "architectures": [
+    "MBartForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "classifier_dropout": 0.0,
+  "d_model": 1024,
+  "decoder_attention_heads": 16,
+  "decoder_ffn_dim": 4096,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 12,
+  "decoder_start_token_id": 2,
+  "dropout": 0.1,
+  "encoder_attention_heads": 16,
+  "encoder_ffn_dim": 4096,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 12,
+  "eos_token_id": 3,
+  "forced_eos_token_id": 3,
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "max_position_embeddings": 1024,
+  "model_type": "mbart",
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "scale_embedding": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "decoder_start_token_id": 2,
+  "eos_token_id": 3,
+  "forced_eos_token_id": 3,
+  "pad_token_id": 0,
+  "transformers_version": "4.38.2"
+}

model.safetensors

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+size 1550557280

special_tokens_map.json

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+{
+  "bos_token": "<BOS>",
+  "eos_token": "<EOS>",
+  "mask_token": "<MASK>",
+  "pad_token": "<PAD>",
+  "unk_token": "<UNK>"
+}

tf_model.h5

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+size 1551059288

tokenizer_config.json

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+{
+  "bos_token": "<BOS>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<EOS>",
+  "mask_token": "<MASK>",
+  "model_max_length": 1024,
+  "pad_token": "<PAD>",
+  "padding_side": "right",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "truncation_side": "right",
+  "unk_token": "<UNK>"
+}