Create README.md

README.md

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+---
+language:
+- vi
+- lo
+tags:
+- translation
+license: mit
+widget:
+- text: "Tôi muốn mua một cuốn sách"
+inference:
+  parameters:
+    max_length: 200
+pipeline_tag: translation
+library_name: transformers
+---
+# Vietnamese to Lao Translation Model
+In the domain of natural language processing (NLP), the development of translation models tailored for low-resource languages represents a critical endeavor to facilitate cross-cultural communication and knowledge exchange. In response to this challenge, we present a novel and impactful contribution: a translation model specifically designed to bridge the linguistic gap between Lao and Vietnamese.
+
+Lao, a language spoken primarily in Laos and parts of Thailand, presents inherent challenges for machine translation due to its low-resource nature, characterized by limited parallel corpora and linguistic resources. Vietnamese, a language spoken by millions worldwide, shares some linguistic similarities with Lao, making it an ideal target language for translation purposes.
+
+Leveraging the power of the Transformer-based T5 model, we have developed a robust translation system for the Vietnamese-Lao language pair. The T5 model, renowned for its versatility and effectiveness across various NLP tasks, serves as the cornerstone of our approach. Through fine-tuning on a curated dataset of Lao-Vietnamese parallel texts, we have endeavored to enhance translation accuracy and fluency, thus enabling smoother communication between speakers of these languages.
+
+Our work represents a significant advancement in the field of machine translation, particularly for low-resource languages like Lao. By harnessing state-of-the-art NLP techniques and focusing on the specific linguistic nuances of the Lao-Vietnamese language pair, we aim to provide a valuable resource for facilitating cross-linguistic communication and cultural exchange.
+## How to use
+### On GPU
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+tokenizer = AutoTokenizer.from_pretrained("minhtoan/t5-translate-lao-vietnamese")  
+model = AutoModelForSeq2SeqLM.from_pretrained("minhtoan/t5-translate-lao-vietnamese")
+model.cuda()
+src = "Tôi muốn mua một cuốn sách"
+tokenized_text = tokenizer.encode(src, return_tensors="pt").cuda()
+model.eval()
+translate_ids = model.generate(tokenized_text, max_length=200)
+output = tokenizer.decode(translate_ids[0], skip_special_tokens=True)
+output
+```
+'ຂ້ອຍຢາກຊື້ປຶ້ມ'
+
+### On CPU
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+tokenizer = AutoTokenizer.from_pretrained("minhtoan/t5-translate-lao-vietnamese")  
+model = AutoModelForSeq2SeqLM.from_pretrained("minhtoan/t5-translate-lao-vietnamese")
+src = "Tôi muốn mua một cuốn sách"
+input_ids = tokenizer(src, max_length=200, return_tensors="pt", padding="max_length", truncation=True).input_ids
+outputs = model.generate(input_ids=input_ids, max_new_tokens=200)
+output = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+output
+```
+'ຂ້ອຍຢາກຊື້ປຶ້ມ'
+
+
+
+## Author
+`
+Phan Minh Toan 
+`