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Browse files- .gitignore +1 -0
- README.md +3 -3
- app.py +113 -0
- requirements.txt +4 -0
.gitignore
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venv/
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README.md
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---
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title:
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emoji:
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colorTo: red
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sdk: streamlit
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app_file: app.py
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---
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title: BERTIN
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emoji: 🔥
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colorFrom: yellow
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colorTo: red
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sdk: streamlit
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app_file: app.py
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app.py
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import random
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from mtranslate import translate
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import streamlit as st
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from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
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LOGO = "https://raw.githubusercontent.com/nlp-en-es/assets/main/logo.png"
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MODELS = {
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"RoBERTa Base": {
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"url": "https://api-inference.huggingface.co/models/bertin-project/bertin-roberta-base-spanish"
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},
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"RoBERTa Base Gaussian": {
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"url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-gaussian"
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},
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"RoBERTa Base Random": {
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"url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-random"
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},
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"RoBERTa Base Stepwise": {
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"url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-stepwise"
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},
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"RoBERTa Base Gaussian Experiment": {
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"url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-gaussian-exp-512seqlen"
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},
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"RoBERTa Base Random Experiment": {
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"url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-random-exp-512seqlen"
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}
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}
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PROMPT_LIST = [
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"Fui a la librería a comprar un <mask>.",
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"¡Qué buen <mask> hace hoy!",
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"Hoy empiezan las vacaciones, vamos a la <mask>.",
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"Mi color favorito es el <mask>.",
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"Voy a <mask>, estoy muy cansada.",
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"Mañana vienen mis amigos de <mask>.",
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"¿Te apetece venir a <mask> conmigo?",
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"En verano hace mucho <mask>.",
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"En el bosque había <mask>."
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]
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@st.cache(show_spinner=False, persist=True)
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def load_model(masked_text, model_name):
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model = AutoModelForMaskedLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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nlp = pipeline("fill-mask", model=model, tokenizer=tokenizer)
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result = nlp(masked_text)
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return result
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# Page
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st.set_page_config(page_title="BERTIN Demo", page_icon=LOGO)
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st.title("BERTIN")
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#Sidebar
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st.sidebar.image(LOGO)
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# Body
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st.markdown(
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"""
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BERTIN is a series of BERT-based models for Spanish.
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The models are trained with Flax and using TPUs sponsored by Google since this is part of the
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[Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104)
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organised by HuggingFace.
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"""
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)
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model_name = st.selectbox("Model",(MODELS.keys()))
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prompt = st.selectbox("Prompt", ["Random", "Custom"])
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if prompt == "Custom":
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prompt_box = "Enter your masked text here..."
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else:
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prompt_box = random.choice(PROMPT_LIST)
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text = st.text_area("Enter text", prompt_box)
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if st.button("Fill the mask"):
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with st.spinner(text="Getting results..."):
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st.subheader("Result")
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result = load_model(text, model_name)
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if "error" in result:
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if type(result["error"]) is str:
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st.write(f'{result["error"]}.', end=" ")
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if "estimated_time" in result:
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st.write(
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f'Please try again in about {result["estimated_time"]:.0f} seconds.'
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)
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else:
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if type(result["error"]) is list:
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for error in result["error"]:
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st.write(f"{error}")
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else:
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result_sequence, result_token = result[0]["sequence"], result[0]["token_str"]
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st.write(result_sequence)
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st.text("English translation")
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st.write(translate(result_sequence, "en", "es"))
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st.markdown(
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"""
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### Team members
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- Javier de la Rosa ([versae](https://huggingface.co/versae))
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- Eduardo González ([edugp](https://huggingface.co/edugp))
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- Paulo Villegas ([paulo](https://huggingface.co/paulo))
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- Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps))
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- Manu Romero ([mrm8488](https://huggingface.co/mrm8488))
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- María Grandury ([mariagrandury](https://huggingface.co/mariagrandury))
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### More information
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You can find more information about these models
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[here](https://huggingface.co/bertin-project/bertin-roberta-base-spanish).
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"""
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)
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requirements.txt
ADDED
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streamlit
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mtranslate
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transformers
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torch
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