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| import random | |
| from mtranslate import translate | |
| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline | |
| LOGO = "https://raw.githubusercontent.com/nlp-en-es/assets/main/logo.png" | |
| MODELS = { | |
| "RoBERTa Base": { | |
| "url": "https://api-inference.huggingface.co/models/bertin-project/bertin-roberta-base-spanish" | |
| }, | |
| "RoBERTa Base Gaussian": { | |
| "url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-gaussian" | |
| }, | |
| "RoBERTa Base Random": { | |
| "url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-random" | |
| }, | |
| "RoBERTa Base Stepwise": { | |
| "url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-stepwise" | |
| }, | |
| "RoBERTa Base Gaussian Experiment": { | |
| "url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-gaussian-exp-512seqlen" | |
| }, | |
| "RoBERTa Base Random Experiment": { | |
| "url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-random-exp-512seqlen" | |
| } | |
| } | |
| PROMPT_LIST = [ | |
| "Fui a la librería a comprar un <mask>.", | |
| "¡Qué buen <mask> hace hoy!", | |
| "Hoy empiezan las vacaciones, vamos a la <mask>.", | |
| "Mi color favorito es el <mask>.", | |
| "Voy a <mask>, estoy muy cansada.", | |
| "Mañana vienen mis amigos de <mask>.", | |
| "¿Te apetece venir a <mask> conmigo?", | |
| "En verano hace mucho <mask>.", | |
| "En el bosque había <mask>." | |
| ] | |
| def load_model(masked_text, model_name): | |
| model = AutoModelForMaskedLM.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| nlp = pipeline("fill-mask", model=model, tokenizer=tokenizer) | |
| result = nlp(masked_text) | |
| return result | |
| # Page | |
| st.set_page_config(page_title="BERTIN Demo", page_icon=LOGO) | |
| st.title("BERTIN") | |
| #Sidebar | |
| st.sidebar.image(LOGO) | |
| # Body | |
| st.markdown( | |
| """ | |
| BERTIN is a series of BERT-based models for Spanish. | |
| The models are trained with Flax and using TPUs sponsored by Google since this is part of the | |
| [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) | |
| organised by HuggingFace. | |
| """ | |
| ) | |
| model_name = st.selectbox("Model",(MODELS.keys())) | |
| prompt = st.selectbox("Prompt", ["Random", "Custom"]) | |
| if prompt == "Custom": | |
| prompt_box = "Enter your masked text here..." | |
| else: | |
| prompt_box = random.choice(PROMPT_LIST) | |
| text = st.text_area("Enter text", prompt_box) | |
| if st.button("Fill the mask"): | |
| with st.spinner(text="Getting results..."): | |
| st.subheader("Result") | |
| result = load_model(text, model_name) | |
| if "error" in result: | |
| if type(result["error"]) is str: | |
| st.write(f'{result["error"]}.', end=" ") | |
| if "estimated_time" in result: | |
| st.write( | |
| f'Please try again in about {result["estimated_time"]:.0f} seconds.' | |
| ) | |
| else: | |
| if type(result["error"]) is list: | |
| for error in result["error"]: | |
| st.write(f"{error}") | |
| else: | |
| result_sequence, result_token = result[0]["sequence"], result[0]["token_str"] | |
| st.write(result_sequence) | |
| st.text("English translation") | |
| st.write(translate(result_sequence, "en", "es")) | |
| st.markdown( | |
| """ | |
| ### Team members | |
| - Javier de la Rosa ([versae](https://huggingface.co/versae)) | |
| - Eduardo González ([edugp](https://huggingface.co/edugp)) | |
| - Paulo Villegas ([paulo](https://huggingface.co/paulo)) | |
| - Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps)) | |
| - Manu Romero ([mrm8488](https://huggingface.co/mrm8488)) | |
| - María Grandury ([mariagrandury](https://huggingface.co/mariagrandury)) | |
| ### More information | |
| You can find more information about these models | |
| [here](https://huggingface.co/bertin-project/bertin-roberta-base-spanish). | |
| """ | |
| ) |