shresthakamal

"""CORS or "Cross-Origin Resource Sharing" refers to the situations when a frontend running in a browser has JavaScript code that communicates with a backend, and the backend is in a different "origin" than the frontend.



Origin
An origin is the combination of protocol (http, https), domain (myapp.com, localhost, localhost.tiangolo.com), and port (80, 443, 8080).

So, all these are different origins:

http://localhost

shresthakamal

// Create an application
  npx create-expo-app rate-repository-app --template expo-template-blank@sdk-50

// Note, that the @sdk-50 sets the project's Expo SDK version to 50, which supports React Native version 0.73. 

// Install dependencies
npx expo install react-native-web@~0.19.6 [email protected] @expo/metro-runtime@~3.1.1


// Start the emulator 

shresthakamal

# Load docs
from langchain.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
data = loader.load()

# Split
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size = 500, chunk_overlap = 0)
all_splits = text_splitter.split_documents(data)

shresthakamal

Show all tmux sessions 
$ tmux ls

Start a new session
$ tmux

Start a new named session
$ tmux new -s <myname>

shresthakamal

https://colab.research.google.com/drive/1yFphU6PW9Uo6lmDly_ud9a6c4RCYlwdX#scrollTo=Mq2PKplWfbFv
https://mccormickml.com/2019/05/14/BERT-word-embeddings-tutorial/#31-running-bert-on-our-text

  
  # -*- coding: utf-8 -*-
"""BERT Word Embeddings v2.ipynb

Automatically generated by Colaboratory.

Original file is located at

shresthakamal

"""
Basic example showing how to read and validate data from a file using Pydantic.
"""

import json
from typing import List, Optional

import pydantic

shresthakamal

import random
import string
from dataclasses import dataclass, field


def generate_id() -> str:
    return "".join(random.choices(string.ascii_uppercase, k=12))


"""

shresthakamal

repos:
-   repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v5.0.0
    hooks:
    -   id: trailing-whitespace
    -   id: end-of-file-fixer
    -   id: check-yaml
    -   id: debug-statements
    -   id: name-tests-test
    -   id: requirements-txt-fixer

shresthakamal

# standard steps to follow for finetunning BERT
# 1. Load the pre-trained model
# 2. Tokenize the input
# 3. Convert the tokens to their index numbers in the BERT vocabulary
# 4. Set all of the model’s parameters to their gradients to zero
# 5. Run the forward pass, calculate the loss, and perform a backward pass to calculate the gradients
# 6. Clip the the gradients to 1.0. It helps in preventing the exploding gradient problem
# 7. Update the model’s parameters
# 8. Update the learning rate.
# 9. Clear the calculated gradients

shresthakamal

# standard pre-processing steps for text processing
# 1. lower case
# 2. remove punctuation
# 3. remove stop words
# 4. remove numbers
# 5. remove short words
# 6. lemmatize
# 7. stem
# 8. remove non-ascii characters
# 9. remove extra spaces