osanseviero

osanseviero

%%capture
!pip install gradio transformers sentencepiece

import gradio as gr

from transformers import pipeline

pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")

def predict(text):

osanseviero

""" Chrome Manifest file: https://developer.chrome.com/docs/extensions/mv3/getstarted/
{
    "manifest_version": 3,
    "name": "Hugging Face Question Generator",
    "version": "1.0",
    "description": "Generate questions based on selected text",
    "permissions": [
        "contextMenus",
        "storage",
        "tabs",

osanseviero

import os
import shutil

from huggingface_hub import  Repository, HfApi, HfFolder

def get_model_card(lang):
    model_card = """---
tags:
- corenlp
library_tag: corenlp

osanseviero

/*
 * Converts table from [[Header0, Header1, Header2], [Column0Val0, Column1Val0, Column2Val0], ...]
 * to {Header0: [ColumnVal0, ...], Header1: [Column1Val0, ...], Header2: [Column2Val0, ...]}
 */
function convertTableToData(table) {
    transposed = table[0].map((_, colIndex) => table.map(row => row[colIndex]));
    result = {}
    for(var i = 0; i < transposed.length; i++) {
      header = transposed[i][0]
      result[header] = transposed[i].slice(1, 4).map(String);