Spaces:
Sleeping
Sleeping
File size: 1,643 Bytes
f399f41 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
from multilingual_translation import text_to_text_generation
from utils import lang_ids, data_scraping
import gradio as gr
lang_list = list(lang_ids.keys())
model_list = data_scraping()
def multilingual_translate(
prompt: str,
model_id: str,
target_lang: str
):
return text_to_text_generation(
prompt=prompt,
model_id=model_id,
device='cpu',
target_lang=lang_ids[target_lang]
)
inputs = [
gr.Textbox(lines=4, value="Hello world!", label="Input Text"),
gr.Dropdown(model_list, value="facebook/m2m100_418M", label="Model"),
gr.Dropdown(lang_list, value="Turkish", label="Target Language"),
]
output = gr.outputs.Textbox(label="Output Text")
examples = [
[
"Hello world!",
"facebook/m2m100_418M",
"Turkish",
],
[
"Omar ve Merve çok iyi arkadaşlar.",
"facebook/m2m100_418M",
"Spanish",
],
[
"Hugging Face is a great company.",
"facebook/m2m100_418M",
"French",
]
]
title = "Beyond English-Centric Multilingual Machine Translation"
description = "M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository."
app = gr.Interface(
fn=multilingual_translate,
inputs=inputs,
outputs=output,
examples=examples,
title=title,
description=description,
cache_examples=True
)
app.launch(debug=True, enable_queue=True)
|