rain-1

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

sbarratt

import pandas as pd
import time
import requests
import time
import hmac
from requests import Request
import sys
import json
import os

remi-or

## Imports
from typing import Tuple
import torch
from torch import Module, Tensor
from transformers.models.roberta.modeling_roberta import RobertaPreTrainedModel, RobertaConfig, RobertaModel, RobertaEncoder
from torch.nn import CrossEntropyLoss, CosineEmbeddingLoss



## Function

shi510

class HardExampleMiner(tf.keras.utils.Sequence):

	def __init__(self, model, x, y, batch_size, map_fn=None, ratio=0.8):
		self.x = np.array(x, dtype=np.float32)
		self.y = np.array(y, dtype=np.float32)
		self.batch_size = batch_size
		self.model = model
		self.ratio = ratio
		self.num_of_batch = int(math.floor((len(self.x) / self.batch_size)))
		self.hard_idxs = np.arange(self.num_of_batch * self.batch_size)

amqdn

felipemoraes

Useful materials:

• Machine Learning cheatsheet: https://stanford.edu/~shervine/teaching/cs-229.html
• Pattern Recognition and Machine Learning Book

ejlp12

import boto3

client = boto3.client('glue')

response = client.create_crawler(
    Name='SalesCSVCrawler',
    Role='AWSGlueServiceRoleDefault',
    DatabaseName='sales-cvs',
    Description='Crawler for generated Sales schema',

yang-zhang

rickcrawford

Create 2 files: main.go and docker-compose.yml. Once both are created use docker-compose up and it will build the necessary files and start Elastic Search.

I found it necessary to add elastic.SetSniff(false) or I could not connect. Also remember the docker containers have security enabled for Elasticsearch with the password set as elastic:changeme. You can test it using curl:

curl http://127.0.0.1:9200/_cat/health -u elastic:changeme

Once it is running you can successfully run the main.go file using go run main.go.

erogol

import torch as th                                                                 
                                                                                   
                                                                                   
class NLL_OHEM(th.nn.NLLLoss):                                                     
    """ Online hard example mining. 
    Needs input from nn.LogSotmax() """                                             
                                                                                   
    def __init__(self, ratio):      
        super(NLL_OHEM, self).__init__(None, True)                                 
        self.ratio = ratio