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  1. MLC_transformer (1).ipynb +2619 -0
  2. labelled_train_set (1).csv +0 -0
  3. model_path.zip +3 -0
  4. requirements.txt +6 -0
  5. unlabelled_test2 (1).csv +0 -0
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1732
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1734
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1735
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1738
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1739
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1743
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1744
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+ {
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+ "name": "stdout",
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+ "text": [
1759
+ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.42.4)\n",
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+ "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.1+cu121)\n",
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+ "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.1.4)\n",
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+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
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+ "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n",
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+ "Requirement already satisfied: numpy<2.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
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+ "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
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+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
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+ "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.6.1)\n",
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+ "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)\n",
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+ " Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
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+ "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)\n",
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+ " Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
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+ "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)\n",
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+ " Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
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+ "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch)\n",
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+ " Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
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+ "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch)\n",
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+ " Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
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+ "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch)\n",
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+ " Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
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+ "Collecting nvidia-curand-cu12==10.3.2.106 (from torch)\n",
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+ " Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
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+ "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch)\n",
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+ " Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
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+ "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch)\n",
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+ " Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
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+ "Collecting nvidia-nccl-cu12==2.20.5 (from torch)\n",
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+ " Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)\n",
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+ "Collecting nvidia-nvtx-cu12==12.1.105 (from torch)\n",
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+ " Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB)\n",
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+ "Requirement already satisfied: triton==2.3.1 in /usr/local/lib/python3.10/dist-packages (from torch) (2.3.1)\n",
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+ "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch)\n",
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+ " Using cached nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
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+ "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
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+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
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+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
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+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n",
1815
+ "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
1816
+ "Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
1817
+ "Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
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+ "Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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+ "Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
1820
+ "Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
1821
+ "Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
1822
+ "Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
1823
+ "Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
1824
+ "Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
1825
+ "Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
1826
+ "Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
1827
+ "Using cached nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl (19.7 MB)\n",
1828
+ "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
1829
+ "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105\n"
1830
+ ]
1831
+ }
1832
+ ]
1833
+ },
1834
+ {
1835
+ "cell_type": "code",
1836
+ "source": [
1837
+ "import pandas as pd\n",
1838
+ "import torch\n",
1839
+ "from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments\n",
1840
+ "from sklearn.model_selection import train_test_split\n",
1841
+ "from sklearn.metrics import accuracy_score, classification_report\n",
1842
+ "from torch.utils.data import Dataset"
1843
+ ],
1844
+ "metadata": {
1845
+ "id": "tXPa3Q0HSsFC"
1846
+ },
1847
+ "execution_count": 2,
1848
+ "outputs": []
1849
+ },
1850
+ {
1851
+ "cell_type": "markdown",
1852
+ "source": [
1853
+ "***PRE-PROCESSING***"
1854
+ ],
1855
+ "metadata": {
1856
+ "id": "9yaeUJ_q3Zsf"
1857
+ }
1858
+ },
1859
+ {
1860
+ "cell_type": "code",
1861
+ "source": [
1862
+ "class TextDataset(Dataset):\n",
1863
+ " def __init__(self, texts, labels, tokenizer, max_len):\n",
1864
+ " self.texts = texts\n",
1865
+ " self.labels = labels\n",
1866
+ " self.tokenizer = tokenizer\n",
1867
+ " self.max_len = max_len\n",
1868
+ "\n",
1869
+ " def __len__(self):\n",
1870
+ " return len(self.texts)\n",
1871
+ "\n",
1872
+ " def __getitem__(self, idx):\n",
1873
+ " text = self.texts[idx]\n",
1874
+ " label = self.labels[idx]\n",
1875
+ " encoding = self.tokenizer.encode_plus(\n",
1876
+ " text,\n",
1877
+ " add_special_tokens=True,\n",
1878
+ " max_length=self.max_len,\n",
1879
+ " padding='max_length',\n",
1880
+ " truncation=True,\n",
1881
+ " return_tensors='pt'\n",
1882
+ " )\n",
1883
+ " return {\n",
1884
+ " 'input_ids': encoding['input_ids'].flatten(),\n",
1885
+ " 'attention_mask': encoding['attention_mask'].flatten(),\n",
1886
+ " 'labels': torch.tensor(label, dtype=torch.long)\n",
1887
+ " }"
1888
+ ],
1889
+ "metadata": {
1890
+ "id": "_5o2TqupSswE"
1891
+ },
1892
+ "execution_count": 3,
1893
+ "outputs": []
1894
+ },
1895
+ {
1896
+ "cell_type": "markdown",
1897
+ "source": [
1898
+ "***BERT - MODEL ***"
1899
+ ],
1900
+ "metadata": {
1901
+ "id": "eJxuyQlk3hrB"
1902
+ }
1903
+ },
1904
+ {
1905
+ "cell_type": "code",
1906
+ "source": [
1907
+ "train_df = pd.read_csv('labelled_train_set (1).csv')\n",
1908
+ "test_df = pd.read_csv('unlabelled_test2 (1).csv')"
1909
+ ],
1910
+ "metadata": {
1911
+ "id": "AaYpOIpHSsyw"
1912
+ },
1913
+ "execution_count": 4,
1914
+ "outputs": []
1915
+ },
1916
+ {
1917
+ "cell_type": "code",
1918
+ "source": [
1919
+ "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
1920
+ ],
1921
+ "metadata": {
1922
+ "id": "STWbzQnNSs1l",
1923
+ "colab": {
1924
+ "base_uri": "https://localhost:8080/",
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+ "height": 269,
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+ "referenced_widgets": [
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+ "801a67b0b1014f149c3fe286084bed6a",
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+ ]
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+ },
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+ "outputId": "89325f36-62b4-458b-c576-55ccc92d7fd3"
1974
+ },
1975
+ "execution_count": 5,
1976
+ "outputs": [
1977
+ {
1978
+ "output_type": "stream",
1979
+ "name": "stderr",
1980
+ "text": [
1981
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
1982
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
1983
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
1984
+ "You will be able to reuse this secret in all of your notebooks.\n",
1985
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
1986
+ " warnings.warn(\n"
1987
+ ]
1988
+ },
1989
+ {
1990
+ "output_type": "display_data",
1991
+ "data": {
1992
+ "text/plain": [
1993
+ "tokenizer_config.json: 0%| | 0.00/48.0 [00:00<?, ?B/s]"
1994
+ ],
1995
+ "application/vnd.jupyter.widget-view+json": {
1996
+ "version_major": 2,
1997
+ "version_minor": 0,
1998
+ "model_id": "801a67b0b1014f149c3fe286084bed6a"
1999
+ }
2000
+ },
2001
+ "metadata": {}
2002
+ },
2003
+ {
2004
+ "output_type": "display_data",
2005
+ "data": {
2006
+ "text/plain": [
2007
+ "vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"
2008
+ ],
2009
+ "application/vnd.jupyter.widget-view+json": {
2010
+ "version_major": 2,
2011
+ "version_minor": 0,
2012
+ "model_id": "55ee667c3dcb41c08400bf90038722d9"
2013
+ }
2014
+ },
2015
+ "metadata": {}
2016
+ },
2017
+ {
2018
+ "output_type": "display_data",
2019
+ "data": {
2020
+ "text/plain": [
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+ "tokenizer.json: 0%| | 0.00/466k [00:00<?, ?B/s]"
2022
+ ],
2023
+ "application/vnd.jupyter.widget-view+json": {
2024
+ "version_major": 2,
2025
+ "version_minor": 0,
2026
+ "model_id": "ca8228676cc04cad8d76bda6442bd168"
2027
+ }
2028
+ },
2029
+ "metadata": {}
2030
+ },
2031
+ {
2032
+ "output_type": "display_data",
2033
+ "data": {
2034
+ "text/plain": [
2035
+ "config.json: 0%| | 0.00/570 [00:00<?, ?B/s]"
2036
+ ],
2037
+ "application/vnd.jupyter.widget-view+json": {
2038
+ "version_major": 2,
2039
+ "version_minor": 0,
2040
+ "model_id": "7cb5a1c1aa1c4b7686c0a4bad4c80361"
2041
+ }
2042
+ },
2043
+ "metadata": {}
2044
+ }
2045
+ ]
2046
+ },
2047
+ {
2048
+ "cell_type": "code",
2049
+ "source": [
2050
+ "label_mapping = {'Human-written': 0, 'AI-generated': 1}\n",
2051
+ "train_df['Type'] = train_df['Type'].map(label_mapping)"
2052
+ ],
2053
+ "metadata": {
2054
+ "id": "ntdnZUKKSs3z"
2055
+ },
2056
+ "execution_count": 6,
2057
+ "outputs": []
2058
+ },
2059
+ {
2060
+ "cell_type": "code",
2061
+ "source": [
2062
+ "X = train_df['Article'].tolist()\n",
2063
+ "y = train_df['Type'].tolist()"
2064
+ ],
2065
+ "metadata": {
2066
+ "id": "LvUVKOKBSs6S"
2067
+ },
2068
+ "execution_count": 7,
2069
+ "outputs": []
2070
+ },
2071
+ {
2072
+ "cell_type": "code",
2073
+ "source": [
2074
+ "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n"
2075
+ ],
2076
+ "metadata": {
2077
+ "id": "lkqV1HffSs8m"
2078
+ },
2079
+ "execution_count": 8,
2080
+ "outputs": []
2081
+ },
2082
+ {
2083
+ "cell_type": "code",
2084
+ "source": [
2085
+ "MAX_LEN = 256"
2086
+ ],
2087
+ "metadata": {
2088
+ "id": "G5oCbeVGSs_P"
2089
+ },
2090
+ "execution_count": 9,
2091
+ "outputs": []
2092
+ },
2093
+ {
2094
+ "cell_type": "code",
2095
+ "source": [
2096
+ "train_dataset = TextDataset(X_train, y_train, tokenizer, MAX_LEN)\n",
2097
+ "val_dataset = TextDataset(X_val, y_val, tokenizer, MAX_LEN)"
2098
+ ],
2099
+ "metadata": {
2100
+ "id": "4ew1JIulStB4"
2101
+ },
2102
+ "execution_count": 10,
2103
+ "outputs": []
2104
+ },
2105
+ {
2106
+ "cell_type": "code",
2107
+ "source": [
2108
+ "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)"
2109
+ ],
2110
+ "metadata": {
2111
+ "colab": {
2112
+ "base_uri": "https://localhost:8080/",
2113
+ "height": 104,
2114
+ "referenced_widgets": [
2115
+ "3c81be5080ec403e90d2f1f26d1713b3",
2116
+ "f95170ab6050497e9867dec502de8d59",
2117
+ "4a3f414ab41e40d2beff1e1a18614c21",
2118
+ "f7aa426fc9484ba2bc1345cafdddf958",
2119
+ "61c34b9db4ac46c3a3f6924cce296c1f",
2120
+ "ca029e26b304491281cafe3ea45242bf",
2121
+ "b74a832501734dfd8431bac7e1d7d3cc",
2122
+ "1f970c7407bf415a904799b0cf23fc72",
2123
+ "b4bc0a2450044e988243e8609180b132",
2124
+ "283e8900cfbc46a9b87fcd360dce0517",
2125
+ "30363ae06da7424790c717993e97061b"
2126
+ ]
2127
+ },
2128
+ "id": "XWFNCnc5StEb",
2129
+ "outputId": "75fd09d3-62fd-4c60-bde3-aabe63c9067f"
2130
+ },
2131
+ "execution_count": 11,
2132
+ "outputs": [
2133
+ {
2134
+ "output_type": "display_data",
2135
+ "data": {
2136
+ "text/plain": [
2137
+ "model.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s]"
2138
+ ],
2139
+ "application/vnd.jupyter.widget-view+json": {
2140
+ "version_major": 2,
2141
+ "version_minor": 0,
2142
+ "model_id": "3c81be5080ec403e90d2f1f26d1713b3"
2143
+ }
2144
+ },
2145
+ "metadata": {}
2146
+ },
2147
+ {
2148
+ "output_type": "stream",
2149
+ "name": "stderr",
2150
+ "text": [
2151
+ "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
2152
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
2153
+ ]
2154
+ }
2155
+ ]
2156
+ },
2157
+ {
2158
+ "cell_type": "markdown",
2159
+ "source": [
2160
+ "***TRAINING THE SET (FITS)***"
2161
+ ],
2162
+ "metadata": {
2163
+ "id": "dkLIt8l13-Oz"
2164
+ }
2165
+ },
2166
+ {
2167
+ "cell_type": "code",
2168
+ "source": [
2169
+ "training_args = TrainingArguments(\n",
2170
+ " output_dir='./results',\n",
2171
+ " num_train_epochs=5,\n",
2172
+ " per_device_train_batch_size=16,\n",
2173
+ " per_device_eval_batch_size=16,\n",
2174
+ " learning_rate=2e-5,\n",
2175
+ " warmup_steps=500,\n",
2176
+ " weight_decay=0.01,\n",
2177
+ " logging_dir='./logs',\n",
2178
+ " logging_steps=10,\n",
2179
+ " eval_strategy=\"steps\",\n",
2180
+ " eval_steps=500,\n",
2181
+ " save_steps=1000,\n",
2182
+ " save_total_limit=2,\n",
2183
+ " load_best_model_at_end=True,\n",
2184
+ " metric_for_best_model=\"accuracy\",\n",
2185
+ ")"
2186
+ ],
2187
+ "metadata": {
2188
+ "id": "8BJS4wEtStGw"
2189
+ },
2190
+ "execution_count": 12,
2191
+ "outputs": []
2192
+ },
2193
+ {
2194
+ "cell_type": "code",
2195
+ "source": [
2196
+ "from transformers import TrainerCallback\n",
2197
+ "\n",
2198
+ "class EarlyStoppingCallback(TrainerCallback):\n",
2199
+ " def __init__(self, early_stopping_patience: int):\n",
2200
+ " self.early_stopping_patience = early_stopping_patience\n",
2201
+ " self.best_score = float('-inf')\n",
2202
+ " self.patience_counter = 0\n",
2203
+ "\n",
2204
+ " def on_evaluate(self, args, state, control, **kwargs):\n",
2205
+ " eval_metrics = kwargs.get('metrics', {})\n",
2206
+ " current_score = eval_metrics.get('eval_accuracy', float('-inf'))\n",
2207
+ "\n",
2208
+ " if current_score > self.best_score:\n",
2209
+ " self.best_score = current_score\n",
2210
+ " self.patience_counter = 0\n",
2211
+ " else:\n",
2212
+ " self.patience_counter += 1\n",
2213
+ "\n",
2214
+ " if self.patience_counter >= self.early_stopping_patience:\n",
2215
+ " control.should_terminate = True\n",
2216
+ "\n",
2217
+ " return control"
2218
+ ],
2219
+ "metadata": {
2220
+ "id": "9dRwLRaz9p-V"
2221
+ },
2222
+ "execution_count": 13,
2223
+ "outputs": []
2224
+ },
2225
+ {
2226
+ "cell_type": "code",
2227
+ "source": [
2228
+ "trainer = Trainer(\n",
2229
+ " model=model,\n",
2230
+ " args=training_args,\n",
2231
+ " train_dataset=train_dataset,\n",
2232
+ " eval_dataset=val_dataset,\n",
2233
+ " callbacks=[EarlyStoppingCallback(early_stopping_patience=3)], # Early stopping to avoid overfitting\n",
2234
+ ")"
2235
+ ],
2236
+ "metadata": {
2237
+ "id": "nUrxO_YwStJF"
2238
+ },
2239
+ "execution_count": 14,
2240
+ "outputs": []
2241
+ },
2242
+ {
2243
+ "cell_type": "code",
2244
+ "source": [
2245
+ "trainer.train()"
2246
+ ],
2247
+ "metadata": {
2248
+ "colab": {
2249
+ "base_uri": "https://localhost:8080/",
2250
+ "height": 110
2251
+ },
2252
+ "id": "oRdQ7YQTStLx",
2253
+ "outputId": "b2e276c4-7099-45bb-e310-228b54a968c7"
2254
+ },
2255
+ "execution_count": 15,
2256
+ "outputs": [
2257
+ {
2258
+ "output_type": "display_data",
2259
+ "data": {
2260
+ "text/plain": [
2261
+ "<IPython.core.display.HTML object>"
2262
+ ],
2263
+ "text/html": [
2264
+ "\n",
2265
+ " <div>\n",
2266
+ " \n",
2267
+ " <progress value='125' max='125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
2268
+ " [125/125 1:24:17, Epoch 5/5]\n",
2269
+ " </div>\n",
2270
+ " <table border=\"1\" class=\"dataframe\">\n",
2271
+ " <thead>\n",
2272
+ " <tr style=\"text-align: left;\">\n",
2273
+ " <th>Step</th>\n",
2274
+ " <th>Training Loss</th>\n",
2275
+ " <th>Validation Loss</th>\n",
2276
+ " </tr>\n",
2277
+ " </thead>\n",
2278
+ " <tbody>\n",
2279
+ " </tbody>\n",
2280
+ "</table><p>"
2281
+ ]
2282
+ },
2283
+ "metadata": {}
2284
+ },
2285
+ {
2286
+ "output_type": "execute_result",
2287
+ "data": {
2288
+ "text/plain": [
2289
+ "TrainOutput(global_step=125, training_loss=0.46063039779663084, metrics={'train_runtime': 5104.3322, 'train_samples_per_second': 0.392, 'train_steps_per_second': 0.024, 'total_flos': 263111055360000.0, 'train_loss': 0.46063039779663084, 'epoch': 5.0})"
2290
+ ]
2291
+ },
2292
+ "metadata": {},
2293
+ "execution_count": 15
2294
+ }
2295
+ ]
2296
+ },
2297
+ {
2298
+ "cell_type": "code",
2299
+ "source": [
2300
+ "eval_results = trainer.evaluate()\n",
2301
+ "print(\"Evaluation Results:\", eval_results)\n",
2302
+ "\n",
2303
+ "if 'eval_accuracy' in eval_results:\n",
2304
+ " print(f\"Validation Accuracy: {eval_results['eval_accuracy']}\")\n",
2305
+ "else:\n",
2306
+ " print(\"AC not found\")\n"
2307
+ ],
2308
+ "metadata": {
2309
+ "id": "fcMtp-HMTR3T",
2310
+ "colab": {
2311
+ "base_uri": "https://localhost:8080/",
2312
+ "height": 92
2313
+ },
2314
+ "outputId": "8c89a269-26b8-437f-e0b9-e583d677a632"
2315
+ },
2316
+ "execution_count": 16,
2317
+ "outputs": [
2318
+ {
2319
+ "output_type": "display_data",
2320
+ "data": {
2321
+ "text/plain": [
2322
+ "<IPython.core.display.HTML object>"
2323
+ ],
2324
+ "text/html": [
2325
+ "\n",
2326
+ " <div>\n",
2327
+ " \n",
2328
+ " <progress value='7' max='7' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
2329
+ " [7/7 01:02]\n",
2330
+ " </div>\n",
2331
+ " "
2332
+ ]
2333
+ },
2334
+ "metadata": {}
2335
+ },
2336
+ {
2337
+ "output_type": "stream",
2338
+ "name": "stdout",
2339
+ "text": [
2340
+ "Evaluation Results: {'eval_loss': 0.2704020142555237, 'eval_runtime': 74.4392, 'eval_samples_per_second': 1.343, 'eval_steps_per_second': 0.094, 'epoch': 5.0}\n",
2341
+ "AC not found\n"
2342
+ ]
2343
+ }
2344
+ ]
2345
+ },
2346
+ {
2347
+ "cell_type": "markdown",
2348
+ "source": [
2349
+ "**`*`FINAL RUN`*`**"
2350
+ ],
2351
+ "metadata": {
2352
+ "id": "XHlI8nUB4Kvv"
2353
+ }
2354
+ },
2355
+ {
2356
+ "cell_type": "code",
2357
+ "source": [
2358
+ "from sklearn.metrics import accuracy_score\n",
2359
+ "\n",
2360
+ "# Make predictions on the validation set\n",
2361
+ "val_predictions = trainer.predict(val_dataset)\n",
2362
+ "val_preds = val_predictions.predictions.argmax(axis=-1)\n",
2363
+ "\n",
2364
+ "# Compute accuracy\n",
2365
+ "val_accuracy = accuracy_score(y_val, val_preds)\n",
2366
+ "print(f\"Validation Accuracy: {val_accuracy}\")\n",
2367
+ "\n",
2368
+ "# classification report\n",
2369
+ "print(\"\\nClassification Report:\")\n",
2370
+ "print(classification_report(y_val, val_preds))\n"
2371
+ ],
2372
+ "metadata": {
2373
+ "colab": {
2374
+ "base_uri": "https://localhost:8080/",
2375
+ "height": 225
2376
+ },
2377
+ "id": "7Z63Jd8KjEq_",
2378
+ "outputId": "cd761333-990a-4889-b3c4-625ac6194721"
2379
+ },
2380
+ "execution_count": 17,
2381
+ "outputs": [
2382
+ {
2383
+ "output_type": "display_data",
2384
+ "data": {
2385
+ "text/plain": [
2386
+ "<IPython.core.display.HTML object>"
2387
+ ],
2388
+ "text/html": []
2389
+ },
2390
+ "metadata": {}
2391
+ },
2392
+ {
2393
+ "output_type": "stream",
2394
+ "name": "stdout",
2395
+ "text": [
2396
+ "Validation Accuracy: 0.96\n",
2397
+ "\n",
2398
+ "Classification Report:\n",
2399
+ " precision recall f1-score support\n",
2400
+ "\n",
2401
+ " 0 1.00 0.95 0.97 77\n",
2402
+ " 1 0.85 1.00 0.92 23\n",
2403
+ "\n",
2404
+ " accuracy 0.96 100\n",
2405
+ " macro avg 0.93 0.97 0.95 100\n",
2406
+ "weighted avg 0.97 0.96 0.96 100\n",
2407
+ "\n"
2408
+ ]
2409
+ }
2410
+ ]
2411
+ },
2412
+ {
2413
+ "cell_type": "markdown",
2414
+ "source": [
2415
+ "***APPLYING THE SAME PROCESSING TO TEST SET***"
2416
+ ],
2417
+ "metadata": {
2418
+ "id": "_I2mZkuT4Pe_"
2419
+ }
2420
+ },
2421
+ {
2422
+ "cell_type": "code",
2423
+ "source": [
2424
+ "class TestDataset(Dataset):\n",
2425
+ " def __init__(self, texts, tokenizer, max_len):\n",
2426
+ " self.texts = texts\n",
2427
+ " self.tokenizer = tokenizer\n",
2428
+ " self.max_len = max_len\n",
2429
+ "\n",
2430
+ " def __len__(self):\n",
2431
+ " return len(self.texts)\n",
2432
+ "\n",
2433
+ " def __getitem__(self, idx):\n",
2434
+ " text = self.texts[idx]\n",
2435
+ " encoding = self.tokenizer.encode_plus(\n",
2436
+ " text,\n",
2437
+ " add_special_tokens=True,\n",
2438
+ " max_length=self.max_len,\n",
2439
+ " padding='max_length',\n",
2440
+ " truncation=True,\n",
2441
+ " return_tensors='pt'\n",
2442
+ " )\n",
2443
+ " return {\n",
2444
+ " 'input_ids': encoding['input_ids'].flatten(),\n",
2445
+ " 'attention_mask': encoding['attention_mask'].flatten()\n",
2446
+ " }"
2447
+ ],
2448
+ "metadata": {
2449
+ "id": "pZJmFa4BTR5n"
2450
+ },
2451
+ "execution_count": 18,
2452
+ "outputs": []
2453
+ },
2454
+ {
2455
+ "cell_type": "markdown",
2456
+ "source": [
2457
+ "# ***`PREDICTIONS ON TEST SET`***"
2458
+ ],
2459
+ "metadata": {
2460
+ "id": "eEM6hUcg4Zty"
2461
+ }
2462
+ },
2463
+ {
2464
+ "cell_type": "code",
2465
+ "source": [
2466
+ "test_dataset = TestDataset(test_df['Article'].tolist(), tokenizer, MAX_LEN)"
2467
+ ],
2468
+ "metadata": {
2469
+ "id": "0Lzt_28ZTR8B"
2470
+ },
2471
+ "execution_count": 19,
2472
+ "outputs": []
2473
+ },
2474
+ {
2475
+ "cell_type": "code",
2476
+ "source": [
2477
+ "predictions = trainer.predict(test_dataset)\n",
2478
+ "pred_labels = predictions.predictions.argmax(axis=-1)\n"
2479
+ ],
2480
+ "metadata": {
2481
+ "id": "yfxQ04G58yuK",
2482
+ "colab": {
2483
+ "base_uri": "https://localhost:8080/",
2484
+ "height": 17
2485
+ },
2486
+ "outputId": "5ffc15a3-101f-47e4-af7c-6ea6dd9c52f4"
2487
+ },
2488
+ "execution_count": 20,
2489
+ "outputs": [
2490
+ {
2491
+ "output_type": "display_data",
2492
+ "data": {
2493
+ "text/plain": [
2494
+ "<IPython.core.display.HTML object>"
2495
+ ],
2496
+ "text/html": []
2497
+ },
2498
+ "metadata": {}
2499
+ }
2500
+ ]
2501
+ },
2502
+ {
2503
+ "cell_type": "markdown",
2504
+ "source": [
2505
+ "**`*SUBMISSION FILE*`**"
2506
+ ],
2507
+ "metadata": {
2508
+ "id": "9jiq3aWX4hym"
2509
+ }
2510
+ },
2511
+ {
2512
+ "cell_type": "code",
2513
+ "source": [
2514
+ "submission_df = pd.DataFrame({\n",
2515
+ " 'ID': test_df['ID'],\n",
2516
+ " 'Predicted Type': pred_labels\n",
2517
+ "})"
2518
+ ],
2519
+ "metadata": {
2520
+ "id": "0Nle0hXV80nR"
2521
+ },
2522
+ "execution_count": 21,
2523
+ "outputs": []
2524
+ },
2525
+ {
2526
+ "cell_type": "code",
2527
+ "source": [
2528
+ "submission_df.to_csv('submission.csv', index=False)"
2529
+ ],
2530
+ "metadata": {
2531
+ "id": "lvb8jI-K896Z"
2532
+ },
2533
+ "execution_count": 22,
2534
+ "outputs": []
2535
+ },
2536
+ {
2537
+ "cell_type": "code",
2538
+ "source": [
2539
+ "# from google.colab import files\n",
2540
+ "# files.download('submission.csv')\n"
2541
+ ],
2542
+ "metadata": {
2543
+ "colab": {
2544
+ "base_uri": "https://localhost:8080/",
2545
+ "height": 17
2546
+ },
2547
+ "id": "WfDPMk9twxKA",
2548
+ "outputId": "5c2509bb-211c-45d1-9409-028727682fa4"
2549
+ },
2550
+ "execution_count": null,
2551
+ "outputs": [
2552
+ {
2553
+ "output_type": "display_data",
2554
+ "data": {
2555
+ "text/plain": [
2556
+ "<IPython.core.display.Javascript object>"
2557
+ ],
2558
+ "application/javascript": [
2559
+ "\n",
2560
+ " async function download(id, filename, size) {\n",
2561
+ " if (!google.colab.kernel.accessAllowed) {\n",
2562
+ " return;\n",
2563
+ " }\n",
2564
+ " const div = document.createElement('div');\n",
2565
+ " const label = document.createElement('label');\n",
2566
+ " label.textContent = `Downloading \"${filename}\": `;\n",
2567
+ " div.appendChild(label);\n",
2568
+ " const progress = document.createElement('progress');\n",
2569
+ " progress.max = size;\n",
2570
+ " div.appendChild(progress);\n",
2571
+ " document.body.appendChild(div);\n",
2572
+ "\n",
2573
+ " const buffers = [];\n",
2574
+ " let downloaded = 0;\n",
2575
+ "\n",
2576
+ " const channel = await google.colab.kernel.comms.open(id);\n",
2577
+ " // Send a message to notify the kernel that we're ready.\n",
2578
+ " channel.send({})\n",
2579
+ "\n",
2580
+ " for await (const message of channel.messages) {\n",
2581
+ " // Send a message to notify the kernel that we're ready.\n",
2582
+ " channel.send({})\n",
2583
+ " if (message.buffers) {\n",
2584
+ " for (const buffer of message.buffers) {\n",
2585
+ " buffers.push(buffer);\n",
2586
+ " downloaded += buffer.byteLength;\n",
2587
+ " progress.value = downloaded;\n",
2588
+ " }\n",
2589
+ " }\n",
2590
+ " }\n",
2591
+ " const blob = new Blob(buffers, {type: 'application/binary'});\n",
2592
+ " const a = document.createElement('a');\n",
2593
+ " a.href = window.URL.createObjectURL(blob);\n",
2594
+ " a.download = filename;\n",
2595
+ " div.appendChild(a);\n",
2596
+ " a.click();\n",
2597
+ " div.remove();\n",
2598
+ " }\n",
2599
+ " "
2600
+ ]
2601
+ },
2602
+ "metadata": {}
2603
+ },
2604
+ {
2605
+ "output_type": "display_data",
2606
+ "data": {
2607
+ "text/plain": [
2608
+ "<IPython.core.display.Javascript object>"
2609
+ ],
2610
+ "application/javascript": [
2611
+ "download(\"download_5382ef96-92a7-48f9-8a52-b0813fdff46b\", \"submission.csv\", 5597)"
2612
+ ]
2613
+ },
2614
+ "metadata": {}
2615
+ }
2616
+ ]
2617
+ }
2618
+ ]
2619
+ }
labelled_train_set (1).csv ADDED
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model_path.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f9530074754bdaf96c05bddf7cb970571d34830c80cc30db352c24ac322c5c6d
3
+ size 405356077
requirements.txt ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ transformers==4.25.1
2
+ torch==1.12.1
3
+ pandas==1.3.3
4
+ streamlit==1.2.0
5
+
6
+
unlabelled_test2 (1).csv ADDED
The diff for this file is too large to render. See raw diff