xprnio

Protocol

Your response must adhere to the following protocol:

1. Empty lines are discarded and not processed
2. Lines prefixed with {command}: are processed as commands.
3. All other lines are processed as messages.

Rules for responding:

1. You are allowed to send messages to the user as well as run commands to do things.
2. You are allowed to send multiple messages to the user from a single response.
3. You are only allowed to send a single sentence per message line.

younesbelkada

import argparse
from mistral.cache import RotatingBufferCache
import torch
import inspect
from typing import List
from pathlib import Path

from mistral.model import Transformer
from mistral.tokenizer import Tokenizer

younesbelkada

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

wiseman

from io import StringIO
import sys
from typing import Dict, Optional

from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents.tools import Tool
from langchain.llms import OpenAI

boganegru

import React from 'react';
import ReactMarkdown from 'react-markdown';
import { withStyles } from '@material-ui/core/styles';
import Typography from '@material-ui/core/Typography';
import Link from '@material-ui/core/Link';
import Table from "@material-ui/core/Table";
import TableContainer from "@material-ui/core/TableContainer";
import Paper from "@material-ui/core/Paper";
import {TableHead, TableRow, TableCell, TableBody} from "@material-ui/core";

dblanchette

import os
from datetime import datetime, timedelta
from typing import Any, Dict, Generator, List, Union

import requests
# Optional - to connect using OAuth credentials
from oauthlib.oauth1 import SIGNATURE_RSA

class JiraClient:
    def __init__(

nikhilkumarsingh

joshfp

p1d1d1

GeoSPARQL support: Triple Stores comparison

I have been planning since months to put in a readable form some tests I did to check how Triple Stores deal with GeoSPARQL. Then a twitter discussion has eventually given me the motivation to start these notes. The word "benchmarking" I used in the mentioned twitter discussion is actually not the right one. It is not my intention to measure performance (how quick is the store to load data, how many trillions triples are managed, how fast is the response to a federated query over tens services): my main interest here is simply to load some geodata and run a basic GeoSPARQL query and see how the stores behave. I decided to use docker and run the images with the default configurations available in Docker Hub: sometimes I have quikly further investigated in case of failure.

Dataset

For the tests I used a simple dataset containing the 26 Swiss Ca

lpillmann

#!/usr/bin/env python
'''Crop an image to just the portions containing text.

Usage:

    ./crop_morphology.py path/to/image.jpg

This will place the cropped image in path/to/image.crop.png.

For details on the methodology, see