Create README.md

README.md

@@ -0,0 +1,77 @@
+---
+language: vi
+datasets:
+- cc100
+tags:
+- summarization
+
+license: mit
+
+widget:
+- text: "ViFortuneAI."
+---
+
+# ViT5-Base Finetuned on `vietnews` Abstractive Summarization (No prefix needed)
+
+
+State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vit5-pretrained-text-to-text-transformer-for/abstractive-text-summarization-on-vietnews)](https://paperswithcode.com/sota/abstractive-text-summarization-on-vietnews?p=vit5-pretrained-text-to-text-transformer-for)
+
+
+## How to use
+For more details, do check out [our Github repo](https://github.com/vietai/ViT5) and [eval script](https://github.com/vietai/ViT5/blob/main/eval/Eval_vietnews_sum.ipynb). 
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load model và tokenizer
+model_name = "ViFortune-AI/ViT5Summer"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+model.cuda()
+
+# DỮ LIỆU ĐẦU VÀO CỦA BẠN: nguyên văn hội thoại (giống trong dataset)
+sentence = "Bạn đã thanh toán cho cà phê không?>> Hmm... tôi nghĩ không phải là vậy, nhưng nó cũng không sao, tôi sẽ thanh toán anh ta mai nhé."
+
+# ✅ KHÔNG thêm "summarize:", KHÔNG thêm "</s>"
+encoding = tokenizer(
+    sentence,
+    return_tensors="pt",
+    max_length=512,
+    truncation=True,
+    padding=False  # hoặc "max_length" nếu muốn
+)
+
+input_ids = encoding["input_ids"].to("cuda")
+attention_mask = encoding["attention_mask"].to("cuda")
+
+# Generate
+outputs = model.generate(
+    input_ids=input_ids,
+    attention_mask=attention_mask,
+    max_length=256,
+    min_length=10,
+    num_beams=4,
+    early_stopping=True,
+    no_repeat_ngram_size=2,
+    length_penalty=1.0
+)
+
+# Decode
+for output in outputs:
+    summary = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+    print("Tóm tắt:", summary)
+```
+
+## Citation
+```
+@inproceedings{phan-etal-2022-vit5,
+    title = "{V}i{T}5: Pretrained Text-to-Text Transformer for {V}ietnamese Language Generation",
+    author = "Phan, Long and Tran, Hieu and Nguyen, Hieu and Trinh, Trieu H.",
+    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop",
+    year = "2022",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.naacl-srw.18",
+    pages = "136--142",
+}
+```