mirror of
https://github.com/hwchase17/langchain
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78 lines
2.0 KiB
Plaintext
78 lines
2.0 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d86853d2",
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"metadata": {},
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"source": [
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"# Twitter (via Apify)\n",
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"\n",
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"This notebook shows how to load chat messages from Twitter to finetune on. We do this by utilizing Apify. \n",
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"\n",
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"First, use Apify to export tweets. An example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "e5034b4e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"from langchain.schema import AIMessage\n",
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"from langchain.adapters.openai import convert_message_to_dict"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "8bf0fb93",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('example_data/dataset_twitter-scraper_2023-08-23_22-13-19-740.json') as f:\n",
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" data = json.load(f)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "468124fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Filter out tweets that reference other tweets, because it's a bit weird\n",
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"tweets = [d[\"full_text\"] for d in data if \"t.co\" not in d['full_text']]\n",
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"# Create them as AI messages\n",
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"messages = [AIMessage(content=t) for t in tweets]\n",
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"# Add in a system message at the start\n",
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"# TODO: we could try to extract the subject from the tweets, and put that in the system message.\n",
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"system_message = {\"role\": \"system\", \"content\": \"write a tweet\"}\n",
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"data = [[system_message, convert_message_to_dict(m)] for m in messages]"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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