gd3kr

"""
a simple script that reads tweets inside a json file, uses openai to compute embeddings and creates two files, metadata.tsv and output.tsv, which cam be used to visualise the tweets and their embeddings in TensorFlow Projector (https://projector.tensorflow.org/)
"""

# obtain tweets.json from https://gist.github.com/gd3kr/948296cf675469f5028911f8eb276dbc

import pandas as pd
import json
from openai import OpenAI

adtac

#!/usr/bin/env -S bash -c "docker run -p 8080:8080 -it --rm \$(docker build --progress plain -f \$0 . 2>&1 | tee /dev/stderr | grep -oP 'sha256:[0-9a-f]*')"

# syntax = docker/dockerfile:1.4.0

FROM node:20

WORKDIR /root

RUN npm install sqlite3

epurdy

NAME OF BELLIGERENT                                Rating +/- Deviation (sorted by Rating - Deviation)

Turkistan Islamic Party                            3587 +/- 113
Dadullah Front                                     3581 +/- 111
High Council of Afghanistan Islamic Emirate        3581 +/- 111
Syrian Arab Republic                               3429 +/- 161
ISIL-YP                                            3250 +/- 166
GPC                                                3233 +/- 166
Saleh                                              3163 +/- 163
Supreme Political Council                          3159 +/- 163

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

mihailik

//@ts-check

async function massBlock() {
  const dummy = {};

  const progressPopup = await showProgressPopup();
  const thumbsUp = '\uD83D\uDC4D';

  async function showProgressPopup() {
    const animationTimeMsec = 200;

ryanfb

#!/usr/bin/env ruby
# Based on: https://github.com/twitterdev/Twitter-API-v2-sample-code/blob/main/Bookmarks-lookup/bookmarks_lookup.rb
# See: https://github.com/ryanfb/twitter-bookmarks-export
require 'json'
require 'typhoeus'
require 'twitter_oauth2'

# First, you will need to enable OAuth 2.0 in your App’s auth settings in the Developer Portal to get your client ID.
# Inside your terminal you will need to set an enviornment variable
# export CLIENT_ID='your-client-id'

radekosmulski

Rocketknight1

# This is a new feature, so make sure to update to the latest version of transformers!
# You will also need to pip install tensorflow_text

import tensorflow as tf
from transformers import TFAutoModel, TFBertTokenizer


class EndToEndModel(tf.keras.Model):
    def __init__(self, checkpoint):
        super().__init__()

pmbaumgartner

from typing import Optional, Iterable, cast, List
from thinc.api import get_current_ops, Ops
from thinc.types import Ragged, Ints1d

from spacy.pipeline.spancat import Suggester
from spacy.tokens import Doc
from spacy.util import registry


@registry.misc("ngram_digits_suggester.v1")

aghasemi

import streamlit.components.v1 as components

_ = components.html(
        """
    <script>
    function loadScript(url)
    {       
        
        return new Promise(function(resolve, reject) {