Update app.py
Browse files
app.py
CHANGED
|
@@ -54,7 +54,7 @@ examples = [
|
|
| 54 |
],
|
| 55 |
]
|
| 56 |
|
| 57 |
-
description = "<br/>The <u><a href='https://huggingface.co/models?search=it5'>IT5 models</a></u> are a family of encoder-decoder models based on the T5 model architecture and pre-trained on a large Italian web corpus. They were introduced in the paper <u><a href='https://
|
| 58 |
|
| 59 |
def model_link(full_id):
|
| 60 |
return f"<a href='https://huggingface.co/{full_id}' target='_blank'><u>🤗 {full_id}<u></a>"
|
|
|
|
| 54 |
],
|
| 55 |
]
|
| 56 |
|
| 57 |
+
description = "<br/>The <u><a href='https://huggingface.co/models?search=it5'>IT5 models</a></u> are a family of encoder-decoder models based on the T5 model architecture and pre-trained on a large Italian web corpus. They were introduced in the paper <u><a href='https://aclanthology.org/2024.lrec-main.823/'>IT5: Text-to-text Pretraining for Italian Language Understanding and Generation</a></u> by <u><a href='https://gsarti.com'>Gabriele Sarti</a></u> and <u><a href='https://malvinanissim.github.io/'>Malvina Nissim</a></u>.<br/><br/>In this demo you can evaluate the whole set of fine-tuned IT5 models and their multilingual counterparts on many sequence-to-sequence tasks for the Italian language (see examples below).<br/><br/>📄 Paper: <u><a href='https://aclanthology.org/2024.lrec-main.823/'>https://aclanthology.org/2024.lrec-main.823/</a></u><br/>💻 Code & Data: <u><a href='https://github.com/gsarti/it5'>https://github.com/gsarti/it5</a></u><br/>🤖 Checkpoints: <u><a href='https://huggingface.co/collections/gsarti/it5-lrec-coling-2024-6600468041d8fee2c42021c8'>https://huggingface.co/collections/gsarti/it5-lrec-coling-2024-6600468041d8fee2c42021c8</a></u>"
|
| 58 |
|
| 59 |
def model_link(full_id):
|
| 60 |
return f"<a href='https://huggingface.co/{full_id}' target='_blank'><u>🤗 {full_id}<u></a>"
|