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Readability and code quality improvements (#959)
* Consistent dataset naming * consistent section headers
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@@ -86,7 +86,8 @@
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"id": "3a84cf35-b37f-4c15-8972-dfafc9fadc1c"
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},
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"source": [
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"## 6.1 Different categories of finetuning"
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" \n",
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"### 6.1 Different categories of finetuning"
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]
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},
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{
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@@ -142,7 +143,8 @@
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"id": "8c7017a2-32aa-4002-a2f3-12aac293ccdf"
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},
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"source": [
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"## 6.2 Preparing the dataset"
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" \n",
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"### 6.2 Preparing the dataset"
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]
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},
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{
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@@ -699,7 +701,8 @@
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"id": "a8d7a0c5-1d5f-458a-b685-3f49520b0094",
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"metadata": {},
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"source": [
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"## 6.3 Creating data loaders"
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" \n",
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"### 6.3 Creating data loaders"
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]
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},
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{
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@@ -1019,7 +1022,8 @@
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"id": "d1c4f61a-5f5d-4b3b-97cf-151b617d1d6c"
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},
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"source": [
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"## 6.4 Initializing a model with pretrained weights"
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" \n",
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"### 6.4 Initializing a model with pretrained weights"
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]
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},
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{
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@@ -1219,7 +1223,8 @@
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"id": "4c9ae440-32f9-412f-96cf-fd52cc3e2522"
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},
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"source": [
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"## 6.5 Adding a classification head"
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" \n",
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"### 6.5 Adding a classification head"
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]
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},
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{
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@@ -1722,7 +1727,8 @@
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"id": "32aa4aef-e1e9-491b-9adf-5aa973e59b8c",
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"metadata": {},
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"source": [
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"## 6.6 Calculating the classification loss and accuracy"
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" \n",
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"### 6.6 Calculating the classification loss and accuracy"
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]
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},
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{
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@@ -2042,7 +2048,8 @@
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"id": "456ae0fd-6261-42b4-ab6a-d24289953083"
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},
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"source": [
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"## 6.7 Finetuning the model on supervised data"
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" \n",
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"### 6.7 Finetuning the model on supervised data"
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]
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},
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{
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@@ -2372,7 +2379,8 @@
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"id": "a74d9ad7-3ec1-450e-8c9f-4fc46d3d5bb0",
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"metadata": {},
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"source": [
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"## 6.8 Using the LLM as a spam classifier"
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" \n",
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"### 6.8 Using the LLM as a spam classifier"
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]
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},
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{
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@@ -2564,6 +2572,7 @@
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"id": "5b70ac71-234f-4eeb-b33d-c62726d50cd4"
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},
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"source": [
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" \n",
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"## Summary and takeaways"
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]
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},
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