import gradio as gr import spaces from transformers import pipeline import torch DESCRIPTION=""" ### a Turkish encoder-decoder language model 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 **Note:** First inference might take time as the models are downloaded on-the-go. *TURNA can generate toxic content or provide erroneous information. Double-check before usage.* """ CITATION = """ Refer to our [paper](https://arxiv.org/abs/2401.14373) for more details. ### Citation ```bibtex @misc{uludoğan2024turna, 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} } ``` """ sentiment_example = [["Bu üründen çok memnun kaldım."]] long_text = [["Eyfel Kulesi (Fransızca: La tour Eiffel [la tuʀ ɛˈfɛl]), Paris'teki demir kule. Kule, aynı zamanda tüm dünyada Fransa'nın sembolü halini almıştır. İsmini, inşa ettiren Fransız inşaat mühendisi Gustave Eiffel'den alır.[1] En büyük turizm cazibelerinden biri olan Eyfel Kulesi, yılda 6 milyon turist çeker. 2002 yılında toplam ziyaretçi sayısı 200 milyona ulaşmıştır."]] ner_example = [["Benim adım Turna."]] t2t_example = [["Paraphrase: Bu üründen çok memnun kaldım."]] nli_example = [["Bunu çok beğendim. Bunu çok sevdim."]] text_category_example = [[" anadolu_efes e 18 lik star ! beko_basketbol_ligi nde iddialı bir kadroyla sezona giren anadolu_efes transfer harekatına devam ediyor"]] @spaces.GPU def nli(input, model_choice="turna_nli_nli_tr"): if model_choice=="turna_nli_nli_tr": nli_model = pipeline(model="boun-tabi-LMG/turna_nli_nli_tr", device=0) return nli_model(input)[0]["generated_text"] else: stsb_model = pipeline(model="boun-tabi-LMG/turna_semantic_similarity_stsb_tr", device=0) return stsb_model(input)[0]["generated_text"] @spaces.GPU def sentiment_analysis(input, model_choice="turna_classification_17bintweet_sentiment"): if model_choice=="turna_classification_17bintweet_sentiment": sentiment_model = pipeline(model="boun-tabi-LMG/turna_classification_17bintweet_sentiment", device=0) return sentiment_model(input)[0]["generated_text"] else: product_reviews = pipeline(model="boun-tabi-LMG/turna_classification_tr_product_reviews", device=0) return product_reviews(input)[0]["generated_text"] @spaces.GPU def pos(input, model_choice="turna_pos_imst"): if model_choice=="turna_pos_imst": pos_imst = pipeline(model="boun-tabi-LMG/turna_pos_imst", device=0) return pos_imst(input)[0]["generated_text"] else: pos_boun = pipeline(model="boun-tabi-LMG/turna_pos_boun", device=0) return pos_boun(input)[0]["generated_text"] @spaces.GPU def ner(input, model_choice="turna_ner_wikiann"): if model_choice=="turna_ner_wikiann": ner_wikiann = pipeline(model="boun-tabi-LMG/turna_ner_wikiann", device=0) return ner_wikiann(input)[0]["generated_text"] else: ner_model = pipeline(model="boun-tabi-LMG/turna_ner_milliyet", device=0) return ner_model(input)[0]["generated_text"] @spaces.GPU def paraphrase(input, model_choice="turna_paraphrasing_tatoeba"): if model_choice=="turna_paraphrasing_tatoeba": paraphrasing = pipeline(model="boun-tabi-LMG/turna_paraphrasing_tatoeba", device=0) return paraphrasing(input)[0]["generated_text"] else: paraphrasing_sub = pipeline(model="boun-tabi-LMG/turna_paraphrasing_opensubtitles", device=0) return paraphrasing_sub(input)[0]["generated_text"] @spaces.GPU def summarize(input, model_choice="turna_summarization_tr_news", max_new_tokens, length_penalty, no_repeat_ngram_size): model_mapping = {"turna_summarization_tr_news": "boun-tabi-LMG/turna_summarization_tr_news", "turna_summarization_mlsum": "boun-tabi-LMG/turna_summarization_mlsum"} summarization_model = pipeline(model=model_mapping[model_choice], device=0) return summarization_model(input, max_new_tokens = max_new_tokens, length_penalty=length_penalty, no_repeat_ngram_size=no_repeat_ngram_size)[0]["generated_text"] @spaces.GPU def categorize(input): ttc = pipeline(model="boun-tabi-LMG/turna_classification_ttc4900", device=0) return ttc(input)[0]["generated_text"] @spaces.GPU def turna(input, max_new_tokens, length_penalty, top_k, top_p, temp, num_beams, do_sample, no_repeat_ngram_size, repetition_penalty): turna = pipeline(model="boun-tabi-LMG/TURNA", device=0) input = f"[S2S] {input}" return turna(input, max_new_tokens = max_new_tokens, length_penalty=length_penalty, top_k=top_k, top_p=top_p, temperature=temp, num_beams=num_beams, do_sample = do_sample, no_repeat_ngram_size=no_repeat_ngram_size, repetition_penalty=repetition_penalty)[0]["generated_text"] with gr.Blocks(theme="abidlabs/Lime") as demo: gr.Markdown("# TURNA") with gr.Row(): gr.Image("images/turna-logo.png", width=100) gr.Markdown(DESCRIPTION) with gr.Tab("Sentiment Analysis"): gr.Markdown("TURNA fine-tuned on sentiment analysis. Enter text to analyse sentiment and pick the model (tweets or product reviews).") with gr.Column(): with gr.Row(): with gr.Column(): sentiment_choice = gr.Radio(choices = ["turna_classification_17bintweet_sentiment", "turna_classification_tr_product_reviews"], label ="Model", value="turna_classification_17bintweet_sentiment") sentiment_input = gr.Textbox(label="Sentiment Analysis Input") sentiment_submit = gr.Button() sentiment_output = gr.Textbox(label="Sentiment Analysis Output") sentiment_submit.click(sentiment_analysis, inputs=[sentiment_input, sentiment_choice], outputs=sentiment_output) sentiment_examples = gr.Examples(examples = sentiment_example, inputs = [sentiment_input, sentiment_choice], outputs=sentiment_output, fn=sentiment_analysis) with gr.Tab("TURNA 🐦"): gr.Markdown("Pre-trained TURNA. Enter text to start generating.") with gr.Column(): with gr.Row(): with gr.Column(): with gr.Accordion("Advanced Generation Parameters"): max_new_tokens = gr.Slider(label = "Maximum length", minimum = 0, maximum = 512, value = 128) length_penalty = gr.Slider(label = "Length penalty", value=1.0) top_k = gr.Slider(label = "Top-k", value=10) top_p = gr.Slider(label = "Top-p", value=0.95) temp = gr.Slider(label = "Temperature", value=1.0, minimum=0.1, maximum=100.0) no_repeat_ngram_size =gr.Slider(label="No Repeat N-Gram Size", minimum=0,value=3,) repetition_penalty = gr.Slider(label = "Repetition Penalty", minimum=0.0, value=3.1, step=0.1) num_beams = gr.Slider(label = "Number of beams", minimum=1, maximum=10, value=3) do_sample = gr.Radio(choices = [True, False], value = True, label = "Sampling") with gr.Column(): text_gen_input = gr.Textbox(label="Text Generation Input") text_gen_submit = gr.Button() text_gen_output = gr.Textbox(label="Text Generation Output") text_gen_submit.click(turna, inputs=[text_gen_input, max_new_tokens, length_penalty, top_k, top_p, temp, num_beams, do_sample, no_repeat_ngram_size, repetition_penalty], outputs=text_gen_output) text_gen_example = [["Bir varmış, bir yokmuş, evvel zaman içinde, kalbur saman içinde, uzak diyarların birinde bir turna"]] text_gen_examples = gr.Examples(examples = text_gen_example, inputs = [text_gen_input, max_new_tokens, length_penalty, top_k, top_p, temp, num_beams, do_sample, no_repeat_ngram_size, repetition_penalty], outputs=text_gen_output, fn=turna) with gr.Tab("Text Categorization"): gr.Markdown("TURNA fine-tuned on text categorization. Enter text to categorize text or try the example.") with gr.Column(): with gr.Row(): with gr.Column(): text_input = gr.Textbox(label="Text Categorization Input") text_submit = gr.Button() text_output = gr.Textbox(label="Text Categorization Output") text_submit.click(categorize, inputs=[text_input], outputs=text_output) text_examples = gr.Examples(examples = text_category_example,inputs=[text_input], outputs=text_output, fn=categorize) with gr.Tab("NLI"): gr.Markdown("TURNA fine-tuned on natural language inference. Enter text to infer entailment and pick the model. You can also check for semantic similarity entailment.") with gr.Column(): with gr.Row(): with gr.Column(): nli_choice = gr.Radio(choices = ["turna_nli_nli_tr", "turna_semantic_similarity_stsb_tr"], label ="Model", value="turna_nli_nli_tr") nli_input = gr.Textbox(label="NLI Input") nli_submit = gr.Button() nli_output = gr.Textbox(label="NLI Output") nli_submit.click(nli, inputs=[nli_input, nli_choice], outputs=nli_output) nli_examples = gr.Examples(examples = nli_example, inputs = [nli_input, nli_choice], outputs=nli_output, fn=nli) with gr.Tab("POS"): gr.Markdown("TURNA fine-tuned on part-of-speech-tagging. Enter text to parse parts of speech and pick the model.") with gr.Column(): with gr.Row(): with gr.Column(): pos_choice = gr.Radio(choices = ["turna_pos_imst", "turna_pos_boun"], label ="Model", value="turna_pos_imst") pos_input = gr.Textbox(label="POS Input") pos_submit = gr.Button() pos_output = gr.Textbox(label="POS Output") pos_submit.click(pos, inputs=[pos_input, pos_choice], outputs=pos_output) pos_examples = gr.Examples(examples = ner_example, inputs = [pos_input, pos_choice], outputs=pos_output, fn=pos) with gr.Tab("NER"): gr.Markdown("TURNA fine-tuned on named entity recognition. Enter text to parse named entities and pick the model.") with gr.Column(): with gr.Row(): with gr.Column(): ner_choice = gr.Radio(choices = ["turna_ner_wikiann", "turna_ner_milliyet"], label ="Model", value="turna_ner_wikiann") ner_input = gr.Textbox(label="NER Input") ner_submit = gr.Button() ner_output = gr.Textbox(label="NER Output") ner_submit.click(ner, inputs=[ner_input, ner_choice], outputs=ner_output) ner_examples = gr.Examples(examples = ner_example, inputs = [ner_input, ner_choice], outputs=ner_output, fn=ner) with gr.Tab("Paraphrase"): gr.Markdown("TURNA fine-tuned on paraphrasing. Enter text to paraphrase and pick the model.") with gr.Column(): with gr.Row(): with gr.Column(): paraphrasing_choice = gr.Radio(choices = ["turna_paraphrasing_tatoeba", "turna_paraphrasing_opensubtitles"], label ="Model", value="turna_paraphrasing_tatoeba") paraphrasing_input = gr.Textbox(label = "Paraphrasing Input") paraphrasing_submit = gr.Button() paraphrasing_output = gr.Text(label="Paraphrasing Output") paraphrasing_submit.click(paraphrase, inputs=[paraphrasing_input, paraphrasing_choice], outputs=paraphrasing_output) paraphrase_examples = gr.Examples(examples = long_text, inputs = [paraphrasing_input, paraphrasing_choice], outputs=paraphrasing_output, fn=paraphrase) with gr.Tab("Summarization"): gr.Markdown("TURNA fine-tuned on summarization. Enter text to summarize and pick the model.") with gr.Column(): with gr.Row(): with gr.Column(): sum_choice = gr.Radio(choices = ["turna_summarization_mlsum", "turna_summarization_tr_news"], label ="Model", value="turna_summarization_mlsum") with gr.Accordion("Advanced Generation Parameters"): max_new_tokens = gr.Slider(label = "Maximum length", minimum = 0, maximum = 512, value = 128) length_penalty = gr.Slider(label = "Length penalty", minimum = -10, maximum = 10, value=2.0) no_repeat_ngram_size =gr.Slider(label="No Repeat N-Gram Size", minimum=0,value=3,) with gr.Column(): sum_input = gr.Textbox(label = "Summarization Input") sum_submit = gr.Button() sum_output = gr.Textbox(label = "Summarization Output") sum_submit.click(summarize, inputs=[sum_input, sum_choice, max_new_tokens, length_penalty, no_repeat_ngram_size], outputs=sum_output) sum_examples = gr.Examples(examples = long_text, inputs = [sum_input, sum_choice, max_new_tokens, length_penalty, no_repeat_ngram_size], outputs=sum_output, fn=summarize) gr.Markdown(CITATION) demo.launch()