Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.
Avoid being a link dump. Try to provide only valuable well tuned information.
Neural network links before starting with transformers.
import pandas as pd | |
import time | |
import requests | |
import time | |
import hmac | |
from requests import Request | |
import sys | |
import json | |
import os |
## 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 |
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) |
import boto3
client = boto3.client('glue')
response = client.create_crawler(
Name='SalesCSVCrawler',
Role='AWSGlueServiceRoleDefault',
DatabaseName='sales-cvs',
Description='Crawler for generated Sales schema',
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
.
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 |