import requests import streamlit as st import time from transformers import pipeline import os from .utils import query def write(): st.markdown( """

TURNA

""", unsafe_allow_html=True, ) st.write("#") col = st.columns(2) col[0].image("images/turna-logo.png", width=100) st.markdown( """

a Turkish encoder-decoder language model

""", unsafe_allow_html=True, ) st.markdown( """ Welcome to our Huggingface space, where you can explore the capabilities of TURNA. **Key Features of TURNA:** - **Powerful Architecture:** TURNA contains 1.1B parameters, and was pre-trained with an encoder-decoder architecture following the UL2 framework on 43B tokens from various domains. - **Diverse Training Data:** Our model is trained on a varied dataset of 43 billion tokens, covering a wide array of domains. - **Broad Applications:** TURNA is fine-tuned for a variety of generation and understanding tasks, including: - Summarization - Paraphrasing - News title generation - Sentiment classification - Text categorization - Named entity recognition - Part-of-speech tagging - Semantic textual similarity - Natural language inference Explore various applications powered by **TURNA** using the **Navigation** bar. Refer to our [paper](https://arxiv.org/abs/2401.14373) for more details. ### Citation ```bibtex @misc{uludogan2024turna, title={TURNA: A Turkish Encoder-Decoder Language Model for Enhanced Understanding and Generation}, author={Gökçe Uludoğan and Zeynep Yirmibeşoğlu Balal and Furkan Akkurt and Melikşah Türker and Onur Güngör and Susan Üsküdarlı}, year={2024}, eprint={2401.14373}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` """) st.markdown( """

TURNA can generate toxic content or provide erroneous information. Double-check before usage.

""", unsafe_allow_html=True, )