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Merge pull request #6 from JeffreyRathgeber/milestone-3

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Files changed (9) hide show
  1. README.md +3 -0
  2. TrainedModels/README.txt +1 -0
  3. TrainedModels/tokenizer/special_tokens_map.json +7 -0
  4. TrainedModels/tokenizer/tokenizer.json +0 -0
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  6. TrainedModels/tokenizer/vocab.txt +0 -0
  7. TrainingAlgo.ipynb +2406 -0
  8. aiprojecttest.py +0 -215
  9. app.py +0 -1
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  # CSUY4613-Assignments
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1843
+ "application/vnd.jupyter.widget-view+json": {
1844
+ "version_major": 2,
1845
+ "version_minor": 0,
1846
+ "model_id": "d0334d8257ce422597da3d29ac22cd4f"
1847
+ }
1848
+ },
1849
+ "metadata": {}
1850
+ },
1851
+ {
1852
+ "output_type": "display_data",
1853
+ "data": {
1854
+ "text/plain": [
1855
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/483 [00:00<?, ?B/s]"
1856
+ ],
1857
+ "application/vnd.jupyter.widget-view+json": {
1858
+ "version_major": 2,
1859
+ "version_minor": 0,
1860
+ "model_id": "ee6313a98fd041c9b755be6b6a94ebff"
1861
+ }
1862
+ },
1863
+ "metadata": {}
1864
+ }
1865
+ ],
1866
+ "source": [
1867
+ "df_train = pd.read_csv('train.csv')\n",
1868
+ "df_test = pd.read_csv('test.csv')\n",
1869
+ "df_test_labels = pd.read_csv('test_labels.csv')\n",
1870
+ "\n",
1871
+ "model_name = \"distilbert-base-uncased\"\n",
1872
+ "\n",
1873
+ "def read_file(f):\n",
1874
+ " texts = f['comment_text'].tolist()\n",
1875
+ " labels = []\n",
1876
+ " for i in range(len(f)):\n",
1877
+ " temp = []\n",
1878
+ " temp.append(f['toxic'][i])\n",
1879
+ " temp.append(f['severe_toxic'][i])\n",
1880
+ " temp.append(f['obscene'][i])\n",
1881
+ " temp.append(f['threat'][i])\n",
1882
+ " temp.append(f['insult'][i])\n",
1883
+ " temp.append(f['identity_hate'][i])\n",
1884
+ " labels.append(temp)\n",
1885
+ " return texts, labels\n",
1886
+ "\n",
1887
+ "train_texts, train_labels = read_file(df_train)\n",
1888
+ "test_texts = df_test['comment_text'].tolist()\n",
1889
+ "test_labels = []\n",
1890
+ "for i in range(len(df_test_labels)):\n",
1891
+ " temp = []\n",
1892
+ " temp.append(df_test_labels['toxic'][i])\n",
1893
+ " temp.append(df_test_labels['severe_toxic'][i])\n",
1894
+ " temp.append(df_test_labels['obscene'][i])\n",
1895
+ " temp.append(df_test_labels['threat'][i])\n",
1896
+ " temp.append(df_test_labels['insult'][i])\n",
1897
+ " temp.append(df_test_labels['identity_hate'][i])\n",
1898
+ " test_labels.append(temp)\n",
1899
+ "\n",
1900
+ "train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)\n",
1901
+ "\n",
1902
+ "tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)\n",
1903
+ "\n",
1904
+ "ind = 0\n",
1905
+ "train_encodings = {'input_ids': [], 'attention_mask': []}\n",
1906
+ "\n",
1907
+ "for i in range(len(train_texts)//16):\n",
1908
+ " temp = tokenizer(train_texts[ind:ind+16], truncation=True, padding=True)\n",
1909
+ " train_encodings['input_ids'] += temp['input_ids']\n",
1910
+ " train_encodings['attention_mask'] += temp['attention_mask']\n",
1911
+ " ind += 16\n",
1912
+ "\n",
1913
+ "ind = 0\n",
1914
+ "val_encodings = {'input_ids': [], 'attention_mask': []}\n",
1915
+ "\n",
1916
+ "for i in range(len(val_texts)//16):\n",
1917
+ " temp = tokenizer(val_texts[ind:ind+16], truncation=True, padding=True)\n",
1918
+ " val_encodings['input_ids'] += temp['input_ids']\n",
1919
+ " val_encodings['attention_mask'] += temp['attention_mask']\n",
1920
+ " ind += 16\n",
1921
+ "\n",
1922
+ "ind = 0\n",
1923
+ "test_encodings = {'input_ids': [], 'attention_mask': []}\n",
1924
+ "\n",
1925
+ "for i in range(len(test_texts)//16):\n",
1926
+ " temp = tokenizer(test_texts[ind:ind+16], truncation=True, padding=True)\n",
1927
+ " test_encodings['input_ids'] += temp['input_ids']\n",
1928
+ " test_encodings['attention_mask'] += temp['attention_mask']\n",
1929
+ " ind += 16\n",
1930
+ "\n",
1931
+ "while True:\n",
1932
+ " if len(train_labels) > len(train_encodings):\n",
1933
+ " train_labels.pop()\n",
1934
+ " else:\n",
1935
+ " break\n",
1936
+ " \n",
1937
+ "while True:\n",
1938
+ " if len(val_labels) > len(val_encodings):\n",
1939
+ " val_labels.pop()\n",
1940
+ " else:\n",
1941
+ " break\n",
1942
+ "\n",
1943
+ "while True:\n",
1944
+ " if len(test_labels) > len(test_encodings):\n",
1945
+ " test_labels.pop()\n",
1946
+ " else:\n",
1947
+ " break\n",
1948
+ "\n",
1949
+ "class dataset(Dataset):\n",
1950
+ " def __init__(self, encodings, labels):\n",
1951
+ " self.encodings = encodings\n",
1952
+ " self.labels = labels\n",
1953
+ " \n",
1954
+ " def __getitem__(self, idx):\n",
1955
+ " item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
1956
+ " item['labels'] = torch.tensor(self.labels[idx])\n",
1957
+ " return item\n",
1958
+ " \n",
1959
+ " def __len__(self):\n",
1960
+ " return(len(self.labels))\n",
1961
+ "\n",
1962
+ "train_dataset_list = [[], [], [], [], [], []]\n",
1963
+ "for i in train_labels:\n",
1964
+ " for j in range(6):\n",
1965
+ " train_dataset_list[j].append(i[j])\n",
1966
+ " \n",
1967
+ "val_dataset_list = [[], [], [], [], [], []]\n",
1968
+ "for i in val_labels:\n",
1969
+ " for j in range(6):\n",
1970
+ " val_dataset_list[j].append(i[j])\n",
1971
+ "\n",
1972
+ "train_dataset_0 = dataset(train_encodings, train_dataset_list[0])\n",
1973
+ "train_dataset_1 = dataset(train_encodings, train_dataset_list[1])\n",
1974
+ "train_dataset_2 = dataset(train_encodings, train_dataset_list[2])\n",
1975
+ "train_dataset_3 = dataset(train_encodings, train_dataset_list[3])\n",
1976
+ "train_dataset_4 = dataset(train_encodings, train_dataset_list[4])\n",
1977
+ "train_dataset_5 = dataset(train_encodings, train_dataset_list[5])\n",
1978
+ "\n",
1979
+ "val_dataset_0 = dataset(val_encodings, val_dataset_list[0])\n",
1980
+ "val_dataset_1 = dataset(val_encodings, val_dataset_list[1])\n",
1981
+ "val_dataset_2 = dataset(val_encodings, val_dataset_list[2])\n",
1982
+ "val_dataset_3 = dataset(val_encodings, val_dataset_list[3])\n",
1983
+ "val_dataset_4 = dataset(val_encodings, val_dataset_list[4])\n",
1984
+ "val_dataset_5 = dataset(val_encodings, val_dataset_list[5])"
1985
+ ]
1986
+ },
1987
+ {
1988
+ "cell_type": "code",
1989
+ "source": [
1990
+ "# save tokenizer\n",
1991
+ "tokenizer.save_pretrained(\"tokenizer.json\")"
1992
+ ],
1993
+ "metadata": {
1994
+ "colab": {
1995
+ "base_uri": "https://localhost:8080/"
1996
+ },
1997
+ "id": "dc8jvgisXLhE",
1998
+ "outputId": "876334df-a494-41fd-bd50-70cdab7936ad"
1999
+ },
2000
+ "execution_count": null,
2001
+ "outputs": [
2002
+ {
2003
+ "output_type": "execute_result",
2004
+ "data": {
2005
+ "text/plain": [
2006
+ "('tokenizer.json/tokenizer_config.json',\n",
2007
+ " 'tokenizer.json/special_tokens_map.json',\n",
2008
+ " 'tokenizer.json/vocab.txt',\n",
2009
+ " 'tokenizer.json/added_tokens.json',\n",
2010
+ " 'tokenizer.json/tokenizer.json')"
2011
+ ]
2012
+ },
2013
+ "metadata": {},
2014
+ "execution_count": 9
2015
+ }
2016
+ ]
2017
+ },
2018
+ {
2019
+ "cell_type": "code",
2020
+ "source": [
2021
+ "# training_args = TrainingArguments(output_dir='Rathgeberj/milestone3_fine_tuned', \n",
2022
+ "# push_to_hub=True, \n",
2023
+ "# num_train_epochs=2, \n",
2024
+ "# per_device_train_batch_size=16, \n",
2025
+ "# per_device_eval_batch_size=64, \n",
2026
+ "# warmup_steps=500, learning_rate=5e-5, \n",
2027
+ "# weight_decay=.01, logging_dir='./logs', \n",
2028
+ "# logging_steps=10)\n",
2029
+ "# /Users/jeffreyrathgeber\n",
2030
+ "\n",
2031
+ "training_args = TrainingArguments(output_dir='results', \n",
2032
+ " num_train_epochs=2, \n",
2033
+ " per_device_train_batch_size=16, \n",
2034
+ " per_device_eval_batch_size=64, \n",
2035
+ " warmup_steps=500, learning_rate=5e-5, \n",
2036
+ " weight_decay=.01, logging_dir='./logs', \n",
2037
+ " logging_steps=10)\n",
2038
+ "\n",
2039
+ "model_0 = DistilBertForSequenceClassification.from_pretrained(model_name)\n",
2040
+ "model_1 = DistilBertForSequenceClassification.from_pretrained(model_name)\n",
2041
+ "model_2 = DistilBertForSequenceClassification.from_pretrained(model_name)\n",
2042
+ "model_3 = DistilBertForSequenceClassification.from_pretrained(model_name)\n",
2043
+ "model_4 = DistilBertForSequenceClassification.from_pretrained(model_name)\n",
2044
+ "model_5 = DistilBertForSequenceClassification.from_pretrained(model_name)"
2045
+ ],
2046
+ "metadata": {
2047
+ "colab": {
2048
+ "base_uri": "https://localhost:8080/",
2049
+ "height": 563,
2050
+ "referenced_widgets": [
2051
+ "a828e2eb329548568ea23df75056ce46",
2052
+ "94f47a73ba5b402b84717807b4f127d9",
2053
+ "66f521247f4c4a608e4a79933ca8b499",
2054
+ "9012d483dc42427189bea7c35c6a9f21",
2055
+ "342182d835634efd9e8dcc16c753602b",
2056
+ "a8c8ad17737448748e212de8bba42b5f",
2057
+ "e414907cc96e4e3d879a6759124fd8fe",
2058
+ "a464bcc2342841588e7384b4c2f10a42",
2059
+ "6cc18dcc7a7d4361a5aac3271d47347a",
2060
+ "cb8c4e778a994ba7bb57f279614ebfe8",
2061
+ "739f16b75f3248489495a606ab3650ab"
2062
+ ]
2063
+ },
2064
+ "id": "1FrCJtT23YVX",
2065
+ "outputId": "5311555b-7789-4bd5-8931-4abc9b70360e"
2066
+ },
2067
+ "execution_count": null,
2068
+ "outputs": [
2069
+ {
2070
+ "output_type": "display_data",
2071
+ "data": {
2072
+ "text/plain": [
2073
+ "Downloading pytorch_model.bin: 0%| | 0.00/268M [00:00<?, ?B/s]"
2074
+ ],
2075
+ "application/vnd.jupyter.widget-view+json": {
2076
+ "version_major": 2,
2077
+ "version_minor": 0,
2078
+ "model_id": "a828e2eb329548568ea23df75056ce46"
2079
+ }
2080
+ },
2081
+ "metadata": {}
2082
+ },
2083
+ {
2084
+ "output_type": "stream",
2085
+ "name": "stderr",
2086
+ "text": [
2087
+ "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight']\n",
2088
+ "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
2089
+ "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
2090
+ "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_classifier.weight']\n",
2091
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
2092
+ "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight']\n",
2093
+ "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
2094
+ "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
2095
+ "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_classifier.weight']\n",
2096
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
2097
+ "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight']\n",
2098
+ "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
2099
+ "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
2100
+ "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_classifier.weight']\n",
2101
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
2102
+ "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight']\n",
2103
+ "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
2104
+ "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
2105
+ "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_classifier.weight']\n",
2106
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
2107
+ "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight']\n",
2108
+ "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
2109
+ "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
2110
+ "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_classifier.weight']\n",
2111
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
2112
+ "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight']\n",
2113
+ "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
2114
+ "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
2115
+ "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_classifier.weight']\n",
2116
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
2117
+ ]
2118
+ }
2119
+ ]
2120
+ },
2121
+ {
2122
+ "cell_type": "code",
2123
+ "source": [
2124
+ "trainer_0 = Trainer(model=model_0, args=training_args, train_dataset=train_dataset_0, eval_dataset=val_dataset_0)\n",
2125
+ "trainer_0.train()\n",
2126
+ "trainer_0.save_model(output_dir='NEW')"
2127
+ ],
2128
+ "metadata": {
2129
+ "colab": {
2130
+ "base_uri": "https://localhost:8080/",
2131
+ "height": 127
2132
+ },
2133
+ "id": "_iaP4g715FZl",
2134
+ "outputId": "feab2bf7-e30a-4282-8f34-4d45bf2baf55"
2135
+ },
2136
+ "execution_count": null,
2137
+ "outputs": [
2138
+ {
2139
+ "output_type": "stream",
2140
+ "name": "stderr",
2141
+ "text": [
2142
+ "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
2143
+ " warnings.warn(\n"
2144
+ ]
2145
+ },
2146
+ {
2147
+ "output_type": "display_data",
2148
+ "data": {
2149
+ "text/plain": [
2150
+ "<IPython.core.display.HTML object>"
2151
+ ],
2152
+ "text/html": [
2153
+ "\n",
2154
+ " <div>\n",
2155
+ " \n",
2156
+ " <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
2157
+ " [2/2 00:04, Epoch 2/2]\n",
2158
+ " </div>\n",
2159
+ " <table border=\"1\" class=\"dataframe\">\n",
2160
+ " <thead>\n",
2161
+ " <tr style=\"text-align: left;\">\n",
2162
+ " <th>Step</th>\n",
2163
+ " <th>Training Loss</th>\n",
2164
+ " </tr>\n",
2165
+ " </thead>\n",
2166
+ " <tbody>\n",
2167
+ " </tbody>\n",
2168
+ "</table><p>"
2169
+ ]
2170
+ },
2171
+ "metadata": {}
2172
+ }
2173
+ ]
2174
+ },
2175
+ {
2176
+ "cell_type": "code",
2177
+ "source": [
2178
+ "trainer_1 = Trainer(model=model_1, args=training_args, train_dataset=train_dataset_1, eval_dataset=val_dataset_1)\n",
2179
+ "trainer_1.train()\n",
2180
+ "trainer_1.save_model(output_dir='UPDATED_1')"
2181
+ ],
2182
+ "metadata": {
2183
+ "colab": {
2184
+ "base_uri": "https://localhost:8080/",
2185
+ "height": 74
2186
+ },
2187
+ "id": "q6hRKNNgGrVB",
2188
+ "outputId": "8dddc83c-4292-424c-8534-61d8125ecac5"
2189
+ },
2190
+ "execution_count": null,
2191
+ "outputs": [
2192
+ {
2193
+ "output_type": "display_data",
2194
+ "data": {
2195
+ "text/plain": [
2196
+ "<IPython.core.display.HTML object>"
2197
+ ],
2198
+ "text/html": [
2199
+ "\n",
2200
+ " <div>\n",
2201
+ " \n",
2202
+ " <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
2203
+ " [2/2 00:09, Epoch 2/2]\n",
2204
+ " </div>\n",
2205
+ " <table border=\"1\" class=\"dataframe\">\n",
2206
+ " <thead>\n",
2207
+ " <tr style=\"text-align: left;\">\n",
2208
+ " <th>Step</th>\n",
2209
+ " <th>Training Loss</th>\n",
2210
+ " </tr>\n",
2211
+ " </thead>\n",
2212
+ " <tbody>\n",
2213
+ " </tbody>\n",
2214
+ "</table><p>"
2215
+ ]
2216
+ },
2217
+ "metadata": {}
2218
+ }
2219
+ ]
2220
+ },
2221
+ {
2222
+ "cell_type": "code",
2223
+ "source": [
2224
+ "trainer_2 = Trainer(model=model_2, args=training_args, train_dataset=train_dataset_2, eval_dataset=val_dataset_2)\n",
2225
+ "trainer_2.train()\n",
2226
+ "trainer_2.save_model(output_dir='UPDATED_2')\n"
2227
+ ],
2228
+ "metadata": {
2229
+ "colab": {
2230
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2231
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2234
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2235
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+ "outputs": [
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+ "output_type": "display_data",
2240
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+ "<IPython.core.display.HTML object>"
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+ "text/html": [
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+ "\n",
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+ " <div>\n",
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+ " \n",
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+ " <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
2249
+ " [2/2 00:03, Epoch 2/2]\n",
2250
+ " </div>\n",
2251
+ " <table border=\"1\" class=\"dataframe\">\n",
2252
+ " <thead>\n",
2253
+ " <tr style=\"text-align: left;\">\n",
2254
+ " <th>Step</th>\n",
2255
+ " <th>Training Loss</th>\n",
2256
+ " </tr>\n",
2257
+ " </thead>\n",
2258
+ " <tbody>\n",
2259
+ " </tbody>\n",
2260
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2261
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2262
+ },
2263
+ "metadata": {}
2264
+ }
2265
+ ]
2266
+ },
2267
+ {
2268
+ "cell_type": "code",
2269
+ "source": [
2270
+ "trainer_3 = Trainer(model=model_3, args=training_args, train_dataset=train_dataset_3, eval_dataset=val_dataset_3)\n",
2271
+ "trainer_3.train()\n",
2272
+ "trainer_3.save_model(output_dir='UPDATED_3')"
2273
+ ],
2274
+ "metadata": {
2275
+ "colab": {
2276
+ "base_uri": "https://localhost:8080/",
2277
+ "height": 74
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2279
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2280
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2282
+ "execution_count": null,
2283
+ "outputs": [
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+ {
2285
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+ "data": {
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+ "text/plain": [
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+ "<IPython.core.display.HTML object>"
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+ ],
2290
+ "text/html": [
2291
+ "\n",
2292
+ " <div>\n",
2293
+ " \n",
2294
+ " <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
2295
+ " [2/2 00:03, Epoch 2/2]\n",
2296
+ " </div>\n",
2297
+ " <table border=\"1\" class=\"dataframe\">\n",
2298
+ " <thead>\n",
2299
+ " <tr style=\"text-align: left;\">\n",
2300
+ " <th>Step</th>\n",
2301
+ " <th>Training Loss</th>\n",
2302
+ " </tr>\n",
2303
+ " </thead>\n",
2304
+ " <tbody>\n",
2305
+ " </tbody>\n",
2306
+ "</table><p>"
2307
+ ]
2308
+ },
2309
+ "metadata": {}
2310
+ }
2311
+ ]
2312
+ },
2313
+ {
2314
+ "cell_type": "code",
2315
+ "source": [
2316
+ "trainer_4 = Trainer(model=model_4, args=training_args, train_dataset=train_dataset_4, eval_dataset=val_dataset_4)\n",
2317
+ "trainer_4.train()\n",
2318
+ "trainer_4.save_model(output_dir='UPDATED_4')"
2319
+ ],
2320
+ "metadata": {
2321
+ "id": "VTJ-4cENGr05",
2322
+ "colab": {
2323
+ "base_uri": "https://localhost:8080/",
2324
+ "height": 74
2325
+ },
2326
+ "outputId": "54cc3a64-c511-4484-c09f-f8ffe2ff65d7"
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+ },
2328
+ "execution_count": null,
2329
+ "outputs": [
2330
+ {
2331
+ "output_type": "display_data",
2332
+ "data": {
2333
+ "text/plain": [
2334
+ "<IPython.core.display.HTML object>"
2335
+ ],
2336
+ "text/html": [
2337
+ "\n",
2338
+ " <div>\n",
2339
+ " \n",
2340
+ " <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
2341
+ " [2/2 00:03, Epoch 2/2]\n",
2342
+ " </div>\n",
2343
+ " <table border=\"1\" class=\"dataframe\">\n",
2344
+ " <thead>\n",
2345
+ " <tr style=\"text-align: left;\">\n",
2346
+ " <th>Step</th>\n",
2347
+ " <th>Training Loss</th>\n",
2348
+ " </tr>\n",
2349
+ " </thead>\n",
2350
+ " <tbody>\n",
2351
+ " </tbody>\n",
2352
+ "</table><p>"
2353
+ ]
2354
+ },
2355
+ "metadata": {}
2356
+ }
2357
+ ]
2358
+ },
2359
+ {
2360
+ "cell_type": "code",
2361
+ "source": [
2362
+ "trainer_5 = Trainer(model=model_5, args=training_args, train_dataset=train_dataset_5, eval_dataset=val_dataset_5)\n",
2363
+ "trainer_5.train()\n",
2364
+ "trainer_5.save_model(output_dir='UPDATED_5')"
2365
+ ],
2366
+ "metadata": {
2367
+ "id": "mgIWDFQ2HtXL",
2368
+ "colab": {
2369
+ "base_uri": "https://localhost:8080/",
2370
+ "height": 74
2371
+ },
2372
+ "outputId": "7cddb662-1c2f-4923-c747-866ec6f3297d"
2373
+ },
2374
+ "execution_count": null,
2375
+ "outputs": [
2376
+ {
2377
+ "output_type": "display_data",
2378
+ "data": {
2379
+ "text/plain": [
2380
+ "<IPython.core.display.HTML object>"
2381
+ ],
2382
+ "text/html": [
2383
+ "\n",
2384
+ " <div>\n",
2385
+ " \n",
2386
+ " <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
2387
+ " [2/2 00:03, Epoch 2/2]\n",
2388
+ " </div>\n",
2389
+ " <table border=\"1\" class=\"dataframe\">\n",
2390
+ " <thead>\n",
2391
+ " <tr style=\"text-align: left;\">\n",
2392
+ " <th>Step</th>\n",
2393
+ " <th>Training Loss</th>\n",
2394
+ " </tr>\n",
2395
+ " </thead>\n",
2396
+ " <tbody>\n",
2397
+ " </tbody>\n",
2398
+ "</table><p>"
2399
+ ]
2400
+ },
2401
+ "metadata": {}
2402
+ }
2403
+ ]
2404
+ }
2405
+ ]
2406
+ }
aiprojecttest.py DELETED
@@ -1,215 +0,0 @@
1
- # -*- coding: utf-8 -*-
2
- """AiProjectTest.ipynb
3
-
4
- Automatically generated by Colaboratory.
5
-
6
- Original file is located at
7
- https://colab.research.google.com/drive/1E4AHYbuRi_FbOMhQntdAMMZMY14hWh2e
8
- """
9
-
10
- from pathlib import Path
11
- from sklearn.model_selection import train_test_split
12
- import torch
13
- from torch.utils.data import Dataset
14
- from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
15
- from transformers import Trainer, TrainingArguments
16
- from torch.utils.data import DataLoader
17
- from transformers import AdamW
18
- import pandas as pd
19
-
20
- df_train = pd.read_csv('train.csv')
21
- df_test = pd.read_csv('test.csv')
22
- df_test_labels = pd.read_csv('test_labels.csv')
23
-
24
- model_name = "distilbert-base-uncased"
25
-
26
- def read_file(f):
27
- texts = f['comment_text'].tolist()
28
- labels = []
29
- for i in range(len(f)):
30
- temp = []
31
- temp.append(f['toxic'][i])
32
- temp.append(f['severe_toxic'][i])
33
- temp.append(f['obscene'][i])
34
- temp.append(f['threat'][i])
35
- temp.append(f['insult'][i])
36
- temp.append(f['identity_hate'][i])
37
- labels.append(temp)
38
- return texts, labels
39
-
40
- train_texts, train_labels = read_file(df_train)
41
- test_texts = df_test['comment_text'].tolist()
42
- test_labels = []
43
- for i in range(len(df_test_labels)):
44
- temp = []
45
- temp.append(df_test_labels['toxic'][i])
46
- temp.append(df_test_labels['severe_toxic'][i])
47
- temp.append(df_test_labels['obscene'][i])
48
- temp.append(df_test_labels['threat'][i])
49
- temp.append(df_test_labels['insult'][i])
50
- temp.append(df_test_labels['identity_hate'][i])
51
- test_labels.append(temp)
52
-
53
- train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)
54
-
55
- tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
56
-
57
- ind = 0
58
- train_encodings = {'input_ids': [], 'attention_mask': []}
59
-
60
- for i in range(len(train_texts)//16):
61
- temp = tokenizer(train_texts[ind:ind+16], truncation=True, padding=True)
62
- train_encodings['input_ids'] += temp['input_ids']
63
- train_encodings['attention_mask'] += temp['attention_mask']
64
- ind += 16
65
-
66
- ind = 0
67
- val_encodings = {'input_ids': [], 'attention_mask': []}
68
-
69
- for i in range(len(val_texts)//16):
70
- temp = tokenizer(val_texts[ind:ind+16], truncation=True, padding=True)
71
- val_encodings['input_ids'] += temp['input_ids']
72
- val_encodings['attention_mask'] += temp['attention_mask']
73
- ind += 16
74
-
75
- ind = 0
76
- test_encodings = {'input_ids': [], 'attention_mask': []}
77
-
78
- for i in range(len(test_texts)//16):
79
- temp = tokenizer(test_texts[ind:ind+16], truncation=True, padding=True)
80
- test_encodings['input_ids'] += temp['input_ids']
81
- test_encodings['attention_mask'] += temp['attention_mask']
82
- ind += 16
83
-
84
- while True:
85
- if len(train_labels) > len(train_encodings):
86
- train_labels.pop()
87
- else:
88
- break
89
-
90
- while True:
91
- if len(val_labels) > len(val_encodings):
92
- val_labels.pop()
93
- else:
94
- break
95
-
96
- while True:
97
- if len(test_labels) > len(test_encodings):
98
- test_labels.pop()
99
- else:
100
- break
101
-
102
- class dataset(Dataset):
103
- def __init__(self, encodings, labels):
104
- self.encodings = encodings
105
- self.labels = labels
106
-
107
- def __getitem__(self, idx):
108
- item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
109
- item['labels'] = torch.tensor(self.labels[idx])
110
- return item
111
-
112
- def __len__(self):
113
- return(len(self.labels))
114
-
115
- train_dataset_list = [[], [], [], [], [], []]
116
- for i in train_labels:
117
- for j in range(6):
118
- train_dataset_list[j].append(i[j])
119
-
120
- val_dataset_list = [[], [], [], [], [], []]
121
- for i in val_labels:
122
- for j in range(6):
123
- val_dataset_list[j].append(i[j])
124
-
125
- train_dataset_0 = dataset(train_encodings, train_dataset_list[0])
126
- train_dataset_1 = dataset(train_encodings, train_dataset_list[1])
127
- train_dataset_2 = dataset(train_encodings, train_dataset_list[2])
128
- train_dataset_3 = dataset(train_encodings, train_dataset_list[3])
129
- train_dataset_4 = dataset(train_encodings, train_dataset_list[4])
130
- train_dataset_5 = dataset(train_encodings, train_dataset_list[5])
131
-
132
- val_dataset_0 = dataset(val_encodings, val_dataset_list[0])
133
- val_dataset_1 = dataset(val_encodings, val_dataset_list[1])
134
- val_dataset_2 = dataset(val_encodings, val_dataset_list[2])
135
- val_dataset_3 = dataset(val_encodings, val_dataset_list[3])
136
- val_dataset_4 = dataset(val_encodings, val_dataset_list[4])
137
- val_dataset_5 = dataset(val_encodings, val_dataset_list[5])
138
-
139
- training_args = TrainingArguments(output_dir='./results',
140
- num_train_epochs=2,
141
- per_device_train_batch_size=16,
142
- per_device_eval_batch_size=64,
143
- warmup_steps=500, learning_rate=5e-5,
144
- weight_decay=.01, logging_dir='./logs',
145
- logging_steps=10)
146
-
147
- model = DistilBertForSequenceClassification.from_pretrained(model_name)
148
-
149
- trainer_0 = Trainer(model=model, args=training_args, train_dataset=train_dataset_0, eval_dataset=val_dataset_0)
150
- trainer_0.train()
151
-
152
- trainer_1 = Trainer(model=model, args=training_args, train_dataset=train_dataset_1, eval_dataset=val_dataset_1)
153
- trainer_1.train()
154
-
155
- trainer_2 = Trainer(model=model, args=training_args, train_dataset=train_dataset_2, eval_dataset=val_dataset_2)
156
- trainer_2.train()
157
-
158
- trainer_3 = Trainer(model=model, args=training_args, train_dataset=train_dataset_3, eval_dataset=val_dataset_3)
159
- trainer_3.train()
160
-
161
- trainer_4 = Trainer(model=model, args=training_args, train_dataset=train_dataset_4, eval_dataset=val_dataset_4)
162
- trainer_4.train()
163
-
164
- trainer_5 = Trainer(model=model, args=training_args, train_dataset=train_dataset_5, eval_dataset=val_dataset_5)
165
- trainer_5.train()
166
-
167
- # train_dataset = dataset(train_encodings, train_labels)
168
- # val_dataset = dataset(val_encodings, val_labels)
169
- # test_dataset = dataset(test_encodings, test_labels)
170
-
171
- # -----------------------------------------------------------------
172
-
173
- # test_dataset_list = [[], [], [], [], [], []]
174
- # for i in test_labels:
175
- # for j in range(6):
176
- # test_dataset_list[j].append(i[j])
177
-
178
- # -----------------------------------------------------------------
179
-
180
- # val_dataset = dataset(val_encodings, val_labels)
181
-
182
- # test_dataset_0 = dataset(test_encodings, test_dataset_list[0])
183
- # test_dataset_1 = dataset(test_encodings, test_dataset_list[1])
184
- # test_dataset_2 = dataset(test_encodings, test_dataset_list[2])
185
- # test_dataset_3 = dataset(test_encodings, test_dataset_list[3])
186
- # test_dataset_4 = dataset(test_encodings, test_dataset_list[4])
187
- # test_dataset_5 = dataset(test_encodings, test_dataset_list[5])
188
-
189
- # -----------------------------------------------------------------
190
-
191
- # device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
192
-
193
- # model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
194
- # model.to(device)
195
- # model.train()
196
-
197
- # train_loader = DataLoader(train_dataset_0, batch_size=16, shuffle=True)
198
-
199
- # optim = AdamW(model.parameters(), lr=5e-5)
200
-
201
- # num_train_epochs = 2
202
- # for epoch in range(num_train_epochs):
203
- # for batch in train_loader:
204
- # optim.zero_grad()
205
- # input_ids = batch['input_ids'].to(device)
206
- # attention_mask = batch['attention_mask'].to(device)
207
- # labels = batch['labels'].to(device)
208
-
209
- # outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
210
-
211
- # loss = outputs[0]
212
- # loss.backward()
213
- # optim.step()
214
-
215
- # model.eval()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
app.py CHANGED
@@ -183,4 +183,3 @@ if option == 'TextBlob':
183
  # tokenizer = AutoTokenizer.from_pretrained(save_directory)
184
  # model = AutoModelForSequenceClassification.from_pretrained(save_directory)
185
 
186
- #------------------------------------------------------------------------
 
183
  # tokenizer = AutoTokenizer.from_pretrained(save_directory)
184
  # model = AutoModelForSequenceClassification.from_pretrained(save_directory)
185