import gradio as gr from transformers import AutoModelForSeq2SeqLM, AutoTokenizer def translate(text): model_name = 'hackathon-pln-es/t5-small-finetuned-spanish-to-quechua' model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) input = tokenizer(text, return_tensors="pt") output = model.generate(input["input_ids"], max_length=40, num_beams=4, early_stopping=True) return tokenizer.decode(output[0], skip_special_tokens=True) title = "Spanish to Quechua translation 🦙" inputs = gr.inputs.Textbox(lines=1, label="Text in Spanish") outputs = [gr.outputs.Textbox(label="Translated text in Quechua")] description = "Here use the [t5-small-finetuned-spanish-to-quechua-model](https://huggingface.co/hackathon-pln-es/t5-small-finetuned-spanish-to-quechua) that was trained with [spanish-to-quechua dataset](https://huggingface.co/datasets/hackathon-pln-es/spanish-to-quechua)." article = ''' ## Challenges - Create a dataset, as there are different variants of Quechua. - Training of the model to optimize results using the least amount of computational resources. ## Team members - [Sara Benel](https://huggingface.co/sbenel) - [Jose Vílchez](https://huggingface.co/JCarlos) ''' examples=[ 'Dios ama a los hombres', 'A pesar de todo, soy feliz', '¿Qué harán allí?', 'Debes aprender a respetar', ] iface = gr.Interface(fn=translate, inputs=inputs, outputs=outputs, theme="dark", examples=examples, title=title, description=description, article=article) iface.launch()