Update README.md

README.md

@@ -1,47 +1,89 @@
 ---
-tags:
-- generated_from_keras_callback
-model-index:
-- name: VBART-XLarge-Summarization
-  results: []
+language:
+- tr
+arXiv: 2403.01308
+library_name: transformers
+pipeline_tag: text2text-generation
+license: cc-by-nc-sa-4.0
 ---
-
-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
-
-# VBART-XLarge-Summarization
-
-This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
-It achieves the following results on the evaluation set:
-
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- optimizer: None
-- training_precision: float32
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.38.2
-- TensorFlow 2.13.1
-- Datasets 2.18.0
-- Tokenizers 0.15.2
+# VBART Model Card
+
+## Model Description  
+
+VBART is the first sequence-to-sequence LLM pre-trained on Turkish corpora from scratch on a large scale. It was pre-trained by VNGRS in February 2023.  
+The model is capable of conditional text generation tasks such as text summarization, paraphrasing, and title generation when fine-tuned.
+It outperforms its multilingual counterparts, albeit being much smaller than other implementations.
+
+VBART-XLarge is created by adding extra Transformer layers between the layers of VBART-Large. Hence it was able to transfer learned weights from the smaller model while doublings its number of layers.
+VBART-XLarge improves the results compared to VBART-Large albeit in small margins.
+
+
+This repository contains fine-tuned TensorFlow and Safetensors weights of VBART for text summarization task.
+
+- **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-XLarge
+- **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-XLarge-Summarization",
+                            model_input_names=['input_ids', 'attention_mask'])
+# Uncomment the device_map kwarg and delete the closing bracket to use model for inference on GPU
+model = AutoModelForSeq2SeqLM.from_pretrained("vngrs-ai/VBART-XLarge-Summarization")#, device_map="auto")
+
+input_text="..."
+
+token_input = tokenizer(input_text, return_tensors="pt")#.to('cuda')
+outputs = model.generate(**token_input)
+print(tokenizer.decode(outputs[0]))
+```
+  
+## Training Details  
+### Training Data  
+The base model is pre-trained on [vngrs-web-corpus](https://huggingface.co/datasets/vngrs-ai/vngrs-web-corpus). It is curated by cleaning and filtering 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 the Turkish sections of [MLSum](https://huggingface.co/datasets/mlsum), [TRNews](https://huggingface.co/datasets/batubayk/TR-News), [XLSum](https://huggingface.co/datasets/csebuetnlp/xlsum) and [Wikilingua](https://huggingface.co/datasets/wiki_lingua) datasets.
+
+### Limitations
+This model is fine-tuned for paraphrasing tasks. 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  
+Pre-trained for 30 days and for a total of 708B tokens. Finetuned for 20 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**: Custom scheduler from the original Transformers paper (20,000 warm-up steps)
+- **Dropout**: 0.1 (dropped to 0.05 and then to 0 in the last 165k and 205k 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**: 1e-5
+-  **Fine-tune epochs**: 20
+
+#### Metrics
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f8b3c84588fe31f435a92b/RY1gfk_XVhMeWKI1-GuCi.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}
+}
+```