Instructions to use minhtoan/t5-translate-lao-vietnamese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minhtoan/t5-translate-lao-vietnamese with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="minhtoan/t5-translate-lao-vietnamese")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("minhtoan/t5-translate-lao-vietnamese") model = AutoModelForSeq2SeqLM.from_pretrained("minhtoan/t5-translate-lao-vietnamese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -13,7 +13,7 @@ inference:
|
|
| 13 |
pipeline_tag: translation
|
| 14 |
library_name: transformers
|
| 15 |
---
|
| 16 |
-
# Lao
|
| 17 |
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.
|
| 18 |
|
| 19 |
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.
|
|
|
|
| 13 |
pipeline_tag: translation
|
| 14 |
library_name: transformers
|
| 15 |
---
|
| 16 |
+
# Lao to Vietnamese Translation Model
|
| 17 |
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.
|
| 18 |
|
| 19 |
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.
|