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fixes typos

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lgvaz 2020-03-31 13:31:37 -03:00
parent 5f20883426
commit df725cfff0
1 changed files with 4 additions and 4 deletions
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10_nlp.ipynb
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@ -1278,7 +1278,7 @@
"source": [ "source": [
"As we have seen at the beginning of this chapter to train a state-of-the-art text classifier using transfer learning will take two steps: first we need to fine-tune our langauge model pretrained on Wikipedia to the corpus of IMDb reviews, then we can use that model to train a classifier.\n", "As we have seen at the beginning of this chapter to train a state-of-the-art text classifier using transfer learning will take two steps: first we need to fine-tune our langauge model pretrained on Wikipedia to the corpus of IMDb reviews, then we can use that model to train a classifier.\n",
"\n", "\n",
"As usual, let's start with assemblng our data." "As usual, let's start with assembling our data."
] ]
}, },
{ {
@ -2157,7 +2157,7 @@
"source": [ "source": [
"Kao estimated that \"less than 800,000 of the 22M+ comments… could be considered truly unique\" and that \"more than 99% of the truly unique comments were in favor of keeping net neutrality.\"\n", "Kao estimated that \"less than 800,000 of the 22M+ comments… could be considered truly unique\" and that \"more than 99% of the truly unique comments were in favor of keeping net neutrality.\"\n",
"\n", "\n",
"Given advances in language modeling that have occurred since 2017, such fraudulent campaigns could be nearly impossible to catch now. You now have all the tools at your disposal necessary to create and compelling language model. That is, something that can generate context appropriate believable text. It won't necessarily be perfectly accurate or correct, but it will be believable. Think about what this technology would mean when put together with the kinds of disinformation campaigns we have learned about. Take a look at this conversation on Reddit shown in <<ethics_reddit>>, where a language model based on OpenAI's GPT-2 algorithm is having a conversation with itself about whether the US government should cut defense spending:" "Given advances in language modeling that have occurred since 2017, such fraudulent campaigns could be nearly impossible to catch now. You now have all the tools at your disposal necessary to create a compelling language model. That is, something that can generate context appropriate believable text. It won't necessarily be perfectly accurate or correct, but it will be believable. Think about what this technology would mean when put together with the kinds of disinformation campaigns we have learned about. Take a look at this conversation on Reddit shown in <<ethics_reddit>>, where a language model based on OpenAI's GPT-2 algorithm is having a conversation with itself about whether the US government should cut defense spending:"
] ]
}, },
{ {
@ -2207,7 +2207,7 @@
"1. What is a language model?\n", "1. What is a language model?\n",
"1. Why is a language model considered self-supervised learning?\n", "1. Why is a language model considered self-supervised learning?\n",
"1. What are self-supervised models usually used for?\n", "1. What are self-supervised models usually used for?\n",
"1. What do we fine-tune language models?\n", "1. Why do we fine-tune language models?\n",
"1. What are the three steps to create a state-of-the-art text classifier?\n", "1. What are the three steps to create a state-of-the-art text classifier?\n",
"1. How do the 50,000 unlabeled movie reviews help create a better text classifier for the IMDb dataset?\n", "1. How do the 50,000 unlabeled movie reviews help create a better text classifier for the IMDb dataset?\n",
"1. What are the three steps to prepare your data for a language model?\n", "1. What are the three steps to prepare your data for a language model?\n",
@ -2262,7 +2262,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.4" "version": "3.7.6"
} }
}, },
"nbformat": 4, "nbformat": 4,