Kewl commited on
Commit
2d8035c
1 Parent(s): 0ae1197

Milestone 3 (#12)

Browse files

* attempts to finetune

* adding fine tuned model

Files changed (3) hide show
  1. .DS_Store +0 -0
  2. app.py +21 -14
  3. fine-tune-toxic-tweets.ipynb +2224 -0
.DS_Store ADDED
Binary file (6.15 kB). View file
 
app.py CHANGED
@@ -1,5 +1,6 @@
1
  import streamlit as st
2
  from transformers import pipeline
 
3
 
4
  st.title("Sentiment Analysis App")
5
 
@@ -7,23 +8,29 @@ text = st.text_area("Input text to get sentiment.", "You are a nice person!")
7
 
8
  model = st.selectbox(
9
  'Select the model you want to use below.',
10
- ("distilbert-base-uncased-finetuned-sst-2-english", "cardiffnlp/twitter-roberta-base-sentiment", "finiteautomata/bertweet-base-sentiment-analysis", "ProsusAI/finbert"))
11
 
12
  classifier = pipeline(task="sentiment-analysis", model=model)
13
 
14
  st.write('You selected:', model)
15
 
16
  if st.button("Get Sentiment"):
17
- prediction = classifier(text)[0]["label"]
18
- if model == "distilbert-base-uncased-finetuned-sst-2-english":
19
- sentiment = prediction
20
- st.write(f"The sentiment is {sentiment}.")
21
- elif model == "cardiffnlp/twitter-roberta-base-sentiment":
22
- sentiment = "NEGATIVE" if prediction == "LABEL_0" else "POSITIVE" if prediction == "LABEL_2" else "NEUTRAL"
23
- st.write(f"The sentiment is {sentiment}.")
24
- elif model == "finiteautomata/bertweet-base-sentiment-analysis":
25
- sentiment = "NEGATIVE" if prediction == "NEG" else "POSITIVE" if prediction == "POS" else "NEUTRAL"
26
- st.write(f"The sentiment is {sentiment}.")
27
- elif model == "ProsusAI/finbert":
28
- sentiment = prediction.upper()
29
- st.write(f"The sentiment is {sentiment}.")
 
 
 
 
 
 
 
1
  import streamlit as st
2
  from transformers import pipeline
3
+ import pandas as pd
4
 
5
  st.title("Sentiment Analysis App")
6
 
 
8
 
9
  model = st.selectbox(
10
  'Select the model you want to use below.',
11
+ ("ac8736/toxic-tweets-fine-tuned-distilbert", "distilbert-base-uncased-finetuned-sst-2-english", "cardiffnlp/twitter-roberta-base-sentiment", "finiteautomata/bertweet-base-sentiment-analysis", "ProsusAI/finbert"))
12
 
13
  classifier = pipeline(task="sentiment-analysis", model=model)
14
 
15
  st.write('You selected:', model)
16
 
17
  if st.button("Get Sentiment"):
18
+ if model != "ac8736/toxic-tweets-fine-tuned-distilbert":
19
+ prediction = classifier(text)[0]["label"]
20
+ if model == "distilbert-base-uncased-finetuned-sst-2-english":
21
+ sentiment = prediction
22
+ st.write(f"The sentiment is {sentiment}.")
23
+ elif model == "cardiffnlp/twitter-roberta-base-sentiment":
24
+ sentiment = "NEGATIVE" if prediction == "LABEL_0" else "POSITIVE" if prediction == "LABEL_2" else "NEUTRAL"
25
+ st.write(f"The sentiment is {sentiment}.")
26
+ elif model == "finiteautomata/bertweet-base-sentiment-analysis":
27
+ sentiment = "NEGATIVE" if prediction == "NEG" else "POSITIVE" if prediction == "POS" else "NEUTRAL"
28
+ st.write(f"The sentiment is {sentiment}.")
29
+ elif model == "ProsusAI/finbert":
30
+ sentiment = prediction.upper()
31
+ st.write(f"The sentiment is {sentiment}.")
32
+ else:
33
+ prediction = classifier(text)[0]
34
+ df = pd.DataFrame([text, prediction['label'], f"{round(prediction['score']*100, 3)}%"])#, columns=["Tweet/Text", "Highest Toxicity", "Probability"])
35
+ st.table(df)
36
+
fine-tune-toxic-tweets.ipynb ADDED
@@ -0,0 +1,2224 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": null,
6
+ "metadata": {
7
+ "colab": {
8
+ "base_uri": "https://localhost:8080/"
9
+ },
10
+ "id": "cYJxJ6jb3xTJ",
11
+ "outputId": "d01a4787-6aea-4026-c6c1-bcbac8ed48ec"
12
+ },
13
+ "outputs": [],
14
+ "source": [
15
+ "# For use with Google Colab only to install transformers library\n",
16
+ "# !pip install transformers"
17
+ ]
18
+ },
19
+ {
20
+ "cell_type": "code",
21
+ "execution_count": 2,
22
+ "metadata": {
23
+ "id": "RqKR2tUd8W2G"
24
+ },
25
+ "outputs": [],
26
+ "source": [
27
+ "# importing necessary libraries\n",
28
+ "\n",
29
+ "import torch \n",
30
+ "from torch.utils.data import Dataset\n",
31
+ "from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification\n",
32
+ "from transformers import AdamW\n",
33
+ "import pandas as pd\n",
34
+ "from sklearn.model_selection import train_test_split\n",
35
+ "from torch.utils.data import DataLoader"
36
+ ]
37
+ },
38
+ {
39
+ "cell_type": "code",
40
+ "execution_count": 3,
41
+ "metadata": {
42
+ "colab": {
43
+ "base_uri": "https://localhost:8080/",
44
+ "height": 206
45
+ },
46
+ "id": "LdZaCOwM8nT4",
47
+ "outputId": "0847ad3f-6088-448d-be7e-43149dee7091"
48
+ },
49
+ "outputs": [
50
+ {
51
+ "data": {
52
+ "text/html": [
53
+ "\n",
54
+ " <div id=\"df-b1c137bc-f80b-4a7f-b5be-c67bd07be223\">\n",
55
+ " <div class=\"colab-df-container\">\n",
56
+ " <div>\n",
57
+ "<style scoped>\n",
58
+ " .dataframe tbody tr th:only-of-type {\n",
59
+ " vertical-align: middle;\n",
60
+ " }\n",
61
+ "\n",
62
+ " .dataframe tbody tr th {\n",
63
+ " vertical-align: top;\n",
64
+ " }\n",
65
+ "\n",
66
+ " .dataframe thead th {\n",
67
+ " text-align: right;\n",
68
+ " }\n",
69
+ "</style>\n",
70
+ "<table border=\"1\" class=\"dataframe\">\n",
71
+ " <thead>\n",
72
+ " <tr style=\"text-align: right;\">\n",
73
+ " <th></th>\n",
74
+ " <th>id</th>\n",
75
+ " <th>comment_text</th>\n",
76
+ " <th>toxic</th>\n",
77
+ " <th>severe_toxic</th>\n",
78
+ " <th>obscene</th>\n",
79
+ " <th>threat</th>\n",
80
+ " <th>insult</th>\n",
81
+ " <th>identity_hate</th>\n",
82
+ " </tr>\n",
83
+ " </thead>\n",
84
+ " <tbody>\n",
85
+ " <tr>\n",
86
+ " <th>0</th>\n",
87
+ " <td>0000997932d777bf</td>\n",
88
+ " <td>Explanation\\r\\nWhy the edits made under my use...</td>\n",
89
+ " <td>0</td>\n",
90
+ " <td>0</td>\n",
91
+ " <td>0</td>\n",
92
+ " <td>0</td>\n",
93
+ " <td>0</td>\n",
94
+ " <td>0</td>\n",
95
+ " </tr>\n",
96
+ " <tr>\n",
97
+ " <th>1</th>\n",
98
+ " <td>000103f0d9cfb60f</td>\n",
99
+ " <td>D'aww! He matches this background colour I'm s...</td>\n",
100
+ " <td>0</td>\n",
101
+ " <td>0</td>\n",
102
+ " <td>0</td>\n",
103
+ " <td>0</td>\n",
104
+ " <td>0</td>\n",
105
+ " <td>0</td>\n",
106
+ " </tr>\n",
107
+ " <tr>\n",
108
+ " <th>2</th>\n",
109
+ " <td>000113f07ec002fd</td>\n",
110
+ " <td>Hey man, I'm really not trying to edit war. It...</td>\n",
111
+ " <td>0</td>\n",
112
+ " <td>0</td>\n",
113
+ " <td>0</td>\n",
114
+ " <td>0</td>\n",
115
+ " <td>0</td>\n",
116
+ " <td>0</td>\n",
117
+ " </tr>\n",
118
+ " <tr>\n",
119
+ " <th>3</th>\n",
120
+ " <td>0001b41b1c6bb37e</td>\n",
121
+ " <td>\"\\r\\nMore\\r\\nI can't make any real suggestions...</td>\n",
122
+ " <td>0</td>\n",
123
+ " <td>0</td>\n",
124
+ " <td>0</td>\n",
125
+ " <td>0</td>\n",
126
+ " <td>0</td>\n",
127
+ " <td>0</td>\n",
128
+ " </tr>\n",
129
+ " <tr>\n",
130
+ " <th>4</th>\n",
131
+ " <td>0001d958c54c6e35</td>\n",
132
+ " <td>You, sir, are my hero. Any chance you remember...</td>\n",
133
+ " <td>0</td>\n",
134
+ " <td>0</td>\n",
135
+ " <td>0</td>\n",
136
+ " <td>0</td>\n",
137
+ " <td>0</td>\n",
138
+ " <td>0</td>\n",
139
+ " </tr>\n",
140
+ " </tbody>\n",
141
+ "</table>\n",
142
+ "</div>\n",
143
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1c137bc-f80b-4a7f-b5be-c67bd07be223')\"\n",
144
+ " title=\"Convert this dataframe to an interactive table.\"\n",
145
+ " style=\"display:none;\">\n",
146
+ " \n",
147
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
148
+ " width=\"24px\">\n",
149
+ " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
150
+ " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
151
+ " </svg>\n",
152
+ " </button>\n",
153
+ " \n",
154
+ " <style>\n",
155
+ " .colab-df-container {\n",
156
+ " display:flex;\n",
157
+ " flex-wrap:wrap;\n",
158
+ " gap: 12px;\n",
159
+ " }\n",
160
+ "\n",
161
+ " .colab-df-convert {\n",
162
+ " background-color: #E8F0FE;\n",
163
+ " border: none;\n",
164
+ " border-radius: 50%;\n",
165
+ " cursor: pointer;\n",
166
+ " display: none;\n",
167
+ " fill: #1967D2;\n",
168
+ " height: 32px;\n",
169
+ " padding: 0 0 0 0;\n",
170
+ " width: 32px;\n",
171
+ " }\n",
172
+ "\n",
173
+ " .colab-df-convert:hover {\n",
174
+ " background-color: #E2EBFA;\n",
175
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
176
+ " fill: #174EA6;\n",
177
+ " }\n",
178
+ "\n",
179
+ " [theme=dark] .colab-df-convert {\n",
180
+ " background-color: #3B4455;\n",
181
+ " fill: #D2E3FC;\n",
182
+ " }\n",
183
+ "\n",
184
+ " [theme=dark] .colab-df-convert:hover {\n",
185
+ " background-color: #434B5C;\n",
186
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
187
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
188
+ " fill: #FFFFFF;\n",
189
+ " }\n",
190
+ " </style>\n",
191
+ "\n",
192
+ " <script>\n",
193
+ " const buttonEl =\n",
194
+ " document.querySelector('#df-b1c137bc-f80b-4a7f-b5be-c67bd07be223 button.colab-df-convert');\n",
195
+ " buttonEl.style.display =\n",
196
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
197
+ "\n",
198
+ " async function convertToInteractive(key) {\n",
199
+ " const element = document.querySelector('#df-b1c137bc-f80b-4a7f-b5be-c67bd07be223');\n",
200
+ " const dataTable =\n",
201
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
202
+ " [key], {});\n",
203
+ " if (!dataTable) return;\n",
204
+ "\n",
205
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
206
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
207
+ " + ' to learn more about interactive tables.';\n",
208
+ " element.innerHTML = '';\n",
209
+ " dataTable['output_type'] = 'display_data';\n",
210
+ " await google.colab.output.renderOutput(dataTable, element);\n",
211
+ " const docLink = document.createElement('div');\n",
212
+ " docLink.innerHTML = docLinkHtml;\n",
213
+ " element.appendChild(docLink);\n",
214
+ " }\n",
215
+ " </script>\n",
216
+ " </div>\n",
217
+ " </div>\n",
218
+ " "
219
+ ],
220
+ "text/plain": [
221
+ " id comment_text toxic \\\n",
222
+ "0 0000997932d777bf Explanation\\r\\nWhy the edits made under my use... 0 \n",
223
+ "1 000103f0d9cfb60f D'aww! He matches this background colour I'm s... 0 \n",
224
+ "2 000113f07ec002fd Hey man, I'm really not trying to edit war. It... 0 \n",
225
+ "3 0001b41b1c6bb37e \"\\r\\nMore\\r\\nI can't make any real suggestions... 0 \n",
226
+ "4 0001d958c54c6e35 You, sir, are my hero. Any chance you remember... 0 \n",
227
+ "\n",
228
+ " severe_toxic obscene threat insult identity_hate \n",
229
+ "0 0 0 0 0 0 \n",
230
+ "1 0 0 0 0 0 \n",
231
+ "2 0 0 0 0 0 \n",
232
+ "3 0 0 0 0 0 \n",
233
+ "4 0 0 0 0 0 "
234
+ ]
235
+ },
236
+ "execution_count": 3,
237
+ "metadata": {},
238
+ "output_type": "execute_result"
239
+ }
240
+ ],
241
+ "source": [
242
+ "# reading in the data and preprocessing the data to create appropriate training data\n",
243
+ "\n",
244
+ "model_name = \"distilbert-base-uncased\"\n",
245
+ "\n",
246
+ "df = pd.read_csv(\"train.csv\")\n",
247
+ "train_texts = df[\"comment_text\"].values\n",
248
+ "train_labels = df[df.columns[2:]].values\n",
249
+ "\n",
250
+ "df.head()"
251
+ ]
252
+ },
253
+ {
254
+ "cell_type": "code",
255
+ "execution_count": 4,
256
+ "metadata": {
257
+ "id": "2oqpgybJ9PQZ"
258
+ },
259
+ "outputs": [],
260
+ "source": [
261
+ "# splitting up the data into training and validation sets\n",
262
+ "\n",
263
+ "train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)"
264
+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 145,
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+ ]
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+ },
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+ "id": "7WgdHb069nsW",
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+ "outputId": "44711fc7-d198-470c-ec3a-d53675ef008e"
322
+ },
323
+ "outputs": [],
324
+ "source": [
325
+ "# creating a custom dataset for training\n",
326
+ "\n",
327
+ "tokenizer = DistilBertTokenizerFast.from_pretrained(model_name, max_length=1024)\n",
328
+ "\n",
329
+ "class ToxicDataset(Dataset):\n",
330
+ " def __init__(self, texts, labels):\n",
331
+ " self.texts = texts\n",
332
+ " self.labels = labels\n",
333
+ " \n",
334
+ " def __getitem__(self, index):\n",
335
+ " encodings = tokenizer(self.texts[index], truncation=True, padding='max_length')\n",
336
+ " item = {key: torch.tensor(val) for key, val in encodings.items()}\n",
337
+ " item['labels'] = torch.tensor(self.labels[index], dtype=torch.float32)\n",
338
+ " del encodings #\n",
339
+ " return item\n",
340
+ "\n",
341
+ " def __len__(self):\n",
342
+ " return len(self.labels)"
343
+ ]
344
+ },
345
+ {
346
+ "cell_type": "code",
347
+ "execution_count": null,
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 156,
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+ "referenced_widgets": [
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+ "2be013220db04444abcc9066a97291c0",
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+ "545cc5df4761445a8bf27abf84bb0254",
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+ "b763f2ed96aa47829a1b9a8c92cc5f0d",
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+ "eb88bdc622924517bf2b679b2160f48e",
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+ "3118cc3daf4e40cda824998d317baaff",
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+ "1d7a678a12d548dcb8059a8a9c067f63",
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+ "89fef94bdbeb4d73b2b959415272d2e1",
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+ "6dbebec0379d46b7a7b4c994132f43a4",
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+ "c8d7c07442e243b58b7375877f091c57",
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+ "b09f8447414047188bf4cc995120c41a",
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+ "a86d27b1fa0d488d9563187f65a87560"
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+ ]
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+ },
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+ "id": "FijVRWyJ91Bo",
367
+ "outputId": "2ab2ef85-ead6-498f-f5cf-574133fcd324"
368
+ },
369
+ "outputs": [],
370
+ "source": [
371
+ "# creating a dataloader for training and custom dataset\n",
372
+ "# device is set in order to use GPU for training, adjust code accordingly if GPU is not available\n",
373
+ "\n",
374
+ "device = torch.device('cuda')\n",
375
+ "\n",
376
+ "model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=6, problem_type=\"multi_label_classification\")\n",
377
+ "model.to(device)\n",
378
+ "model.train()\n",
379
+ "\n",
380
+ "train_dataset = ToxicDataset(train_texts, train_labels)\n",
381
+ "train_dataloader = DataLoader(train_dataset, batch_size=16)"
382
+ ]
383
+ },
384
+ {
385
+ "cell_type": "code",
386
+ "execution_count": null,
387
+ "metadata": {
388
+ "colab": {
389
+ "base_uri": "https://localhost:8080/"
390
+ },
391
+ "id": "PASPUG053GHV",
392
+ "outputId": "d07607e9-ac33-451f-c95d-c3b068d68070"
393
+ },
394
+ "outputs": [],
395
+ "source": [
396
+ "# getting the optimizer and setting the number of epochs\n",
397
+ "\n",
398
+ "optim = AdamW(model.parameters(), lr=5e-5)\n",
399
+ "num_train_epochs = 1"
400
+ ]
401
+ },
402
+ {
403
+ "cell_type": "code",
404
+ "execution_count": 8,
405
+ "metadata": {
406
+ "id": "i3X2d_SaRXsm"
407
+ },
408
+ "outputs": [],
409
+ "source": [
410
+ "# training the model\n",
411
+ "\n",
412
+ "for epoch in range(num_train_epochs):\n",
413
+ " for batch in train_dataloader:\n",
414
+ " optim.zero_grad()\n",
415
+ " input_ids = batch['input_ids'].to(device)\n",
416
+ " attention_mask = batch['attention_mask'].to(device)\n",
417
+ " labels = batch['labels'].to(device)\n",
418
+ "\n",
419
+ " outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
420
+ "\n",
421
+ " loss = outputs[0]\n",
422
+ " loss.backward()\n",
423
+ " optim.step()"
424
+ ]
425
+ },
426
+ {
427
+ "cell_type": "code",
428
+ "execution_count": null,
429
+ "metadata": {
430
+ "id": "ySTMOMGj-giU"
431
+ },
432
+ "outputs": [],
433
+ "source": [
434
+ "model.eval()"
435
+ ]
436
+ },
437
+ {
438
+ "cell_type": "code",
439
+ "execution_count": 17,
440
+ "metadata": {
441
+ "colab": {
442
+ "base_uri": "https://localhost:8080/"
443
+ },
444
+ "id": "-O3TOe49MXar",
445
+ "outputId": "ed8d6f79-9d6a-4353-a05d-17d27d782865"
446
+ },
447
+ "outputs": [
448
+ {
449
+ "name": "stdout",
450
+ "output_type": "stream",
451
+ "text": [
452
+ "tensor([[99.9134, 47.9581, 99.0946, 0.6099, 91.4176, 1.0425]],\n",
453
+ " device='cuda:0')\n"
454
+ ]
455
+ }
456
+ ],
457
+ "source": [
458
+ "# testing a predication on a single example from the training set\n",
459
+ "\n",
460
+ "X_train = [\"COCKSUCKER BEFORE YOU PISS AROUND ON MY WORK\"]\n",
461
+ "batch = tokenizer(X_train, truncation=True, padding='max_length', return_tensors=\"pt\").to(device)\n",
462
+ "\n",
463
+ "with torch.no_grad():\n",
464
+ " outputs = model(**batch)\n",
465
+ " predictions = torch.sigmoid(outputs.logits)*100\n",
466
+ " print(predictions)"
467
+ ]
468
+ },
469
+ {
470
+ "cell_type": "code",
471
+ "execution_count": 18,
472
+ "metadata": {
473
+ "id": "XxIRAUvwM9uS"
474
+ },
475
+ "outputs": [],
476
+ "source": [
477
+ "# saving the model and its tokenizer\n",
478
+ "\n",
479
+ "model.save_pretrained(\"pretrained_model\")\n",
480
+ "tokenizer.save_pretrained(\"model_tokenizer\")"
481
+ ]
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+ }
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+ ],
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+ "metadata": {
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+ "accelerator": "GPU",
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+ "colab": {
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+ "gpuType": "T4",
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+ "provenance": []
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+ "gpuClass": "standard",
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+ "kernelspec": {
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+ "display_name": "Python 3",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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