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"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "GnApaC25Ewx7"
},
"outputs": [],
"source": [
"# pip install accelerate -U"
]
},
{
"cell_type": "code",
"source": [
"# pip install transformers"
],
"metadata": {
"id": "Q3RSJABQGV0t"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# pip install datasets"
],
"metadata": {
"id": "GCIblhncGRsa"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"\n",
"# Upload an image file\n",
"uploaded_workflow = files.upload()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 73
},
"id": "CUL0YNODro_N",
"outputId": "5763d4d2-a173-4822-948d-d5cba10ca870"
},
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/html": [
"\n",
" \n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" \n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving workflow.JPG to workflow (1).JPG\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Get the file name\n",
"workflow = list(uploaded_workflow.keys())[0]\n",
"\n",
"# Display the uploaded image\n",
"from IPython.display import Image\n",
"Image(workflow)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 541
},
"id": "1kGzbI_gsFuS",
"outputId": "22348cd2-1ba4-445b-a6f1-0d7001b1b38e"
},
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"source": [
"# First we need to create a function that will randomly divide the single data file into training, testing and validation"
],
"metadata": {
"id": "4pFydNQgUsE8"
}
},
{
"cell_type": "code",
"source": [
"import random\n",
"\n",
"# For our testing dataset\n",
"\n",
"def random_sample_testing(input_file, output_file, sample_percentage=0.2):\n",
" with open(input_file, 'r', encoding='utf-8') as file:\n",
" lines = file.readlines()\n",
"\n",
" # Extract header and content separately\n",
" header = lines[0]\n",
" content = lines[1:]\n",
"\n",
" num_lines = len(content)\n",
" sample_size = int(num_lines * sample_percentage)\n",
"\n",
" sampled_lines = random.sample(content, sample_size)\n",
"\n",
" # Add the \"TEXT\" header back at the beginning\n",
" sampled_lines = [header] + sampled_lines\n",
"\n",
" with open(output_file, 'w', encoding='utf-8') as file:\n",
" file.writelines(sampled_lines)\n",
"\n",
"# Example usage\n",
"input_document = \"/content/Pre-training CDPtrainfileutfcopy.csv\"\n",
"output_sample = \"/content/testing_dataset.csv\"\n",
"random_sample_testing(input_document, output_sample, sample_percentage=0.2)\n",
"\n",
"\n",
"\n",
"# For our training dataset\n",
"\n",
"def random_sample_testing(input_file, output_file, sample_percentage=0.7):\n",
" with open(input_file, 'r', encoding='utf-8') as file:\n",
" lines = file.readlines()\n",
"\n",
" # Extract header and content separately\n",
" header = lines[0]\n",
" content = lines[1:]\n",
"\n",
" num_lines = len(content)\n",
" sample_size = int(num_lines * sample_percentage)\n",
"\n",
" sampled_lines = random.sample(content, sample_size)\n",
"\n",
" # Add the \"TEXT\" header back at the beginning\n",
" sampled_lines = [header] + sampled_lines\n",
"\n",
" with open(output_file, 'w', encoding='utf-8') as file:\n",
" file.writelines(sampled_lines)\n",
"\n",
"# Example usage\n",
"input_document = \"/content/Pre-training CDPtrainfileutfcopy.csv\"\n",
"output_sample = \"/content/training_dataset.csv\"\n",
"random_sample_testing(input_document, output_sample, sample_percentage=0.7)\n",
"\n",
"\n",
"# For our validation dataset\n",
"\n",
"\n",
"def random_sample_testing(input_file, output_file, sample_percentage=0.1):\n",
" with open(input_file, 'r', encoding='utf-8') as file:\n",
" lines = file.readlines()\n",
"\n",
" # Extract header and content separately\n",
" header = lines[0]\n",
" content = lines[1:]\n",
"\n",
" num_lines = len(content)\n",
" sample_size = int(num_lines * sample_percentage)\n",
"\n",
" sampled_lines = random.sample(content, sample_size)\n",
"\n",
" # Add the \"TEXT\" header back at the beginning\n",
" sampled_lines = [header] + sampled_lines\n",
"\n",
" with open(output_file, 'w', encoding='utf-8') as file:\n",
" file.writelines(sampled_lines)\n",
"\n",
"# Example usage\n",
"input_document = \"/content/Pre-training CDPtrainfileutfcopy.csv\"\n",
"output_sample = \"/content/validation_dataset.csv\"\n",
"random_sample_testing(input_document, output_sample, sample_percentage=0.1)"
],
"metadata": {
"id": "v_R07aAKYLMP"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## We now have our seperate datafiles for training, testing and validation"
],
"metadata": {
"id": "Hvwz9_GFV4Ng"
}
},
{
"cell_type": "code",
"source": [
"from transformers import AutoTokenizer\n",
"from transformers import Trainer, TrainingArguments\n",
"from transformers import RobertaTokenizer, RobertaForMaskedLM\n",
"from transformers import AutoModelForMaskedLM\n",
"from transformers import DataCollatorForLanguageModeling\n",
"from sklearn.model_selection import train_test_split\n",
"from datasets import load_dataset\n",
"import pandas as pd\n",
"from transformers import DataCollatorWithPadding\n",
"\n",
"import random\n",
"import torch\n",
"import math\n",
"\n",
"data_files = {\"train\": \"/content/training_dataset.csv\",\n",
" \"validation\": \"/content/validation_dataset.csv\",\n",
" \"test\": \"/content/testing_dataset.csv\"\n",
" }\n",
"df_Mask = load_dataset(\"csv\", data_files=data_files)\n",
"df_Mask"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 356,
"referenced_widgets": [
"2a9558f4e14945438d66daf4ed2c4542",
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"4ba21b12049e40b2a346495c814e681d",
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"899d872ac43f4bcaa942f8d7d7dd6da3"
]
},
"id": "uY7NjOmlUSaX",
"outputId": "21d7a3d0-9fa3-4a59-e16f-87374df8fe52"
},
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating train split: 0 examples [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "2a9558f4e14945438d66daf4ed2c4542"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating validation split: 0 examples [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "4d35ef97794f4813bf725c46a9161236"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating test split: 0 examples [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "bcd932dbd6284428ac87ee6aaec9d838"
}
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['text'],\n",
" num_rows: 2646\n",
" })\n",
" validation: Dataset({\n",
" features: ['text'],\n",
" num_rows: 378\n",
" })\n",
" test: Dataset({\n",
" features: ['text'],\n",
" num_rows: 756\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"source": [
"# Viewing the dictionary to see that they are actually what we want"
],
"metadata": {
"id": "jCOQAicPa7-x"
}
},
{
"cell_type": "code",
"source": [
"print(\"================================================================================\")\n",
"print(\"Training Data\")\n",
"print(df_Mask['train'][0])\n",
"print(\"================================================================================\")\n",
"print(\"Testing Data\")\n",
"print(df_Mask['test'][0])\n",
"print(\"================================================================================\")\n",
"print(\"Validation Data\")\n",
"print(df_Mask['validation'][0])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0apfI-QzbC_U",
"outputId": "381de4d9-a584-4f79-b50f-6fbf7134323c"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"================================================================================\n",
"Training Data\n",
"{'text': 'This target was approved as science-based under the 1.5C trajectory by the Science-Based Targets initiative in Aug, 2020. The target base year is Fiscal Year 2020, which covers Mar 2019 to Feb 2020. Target year is Fiscal Year 2031, which covers Mar 2030 to Feb 2031.'}\n",
"================================================================================\n",
"Testing Data\n",
"{'text': \"Emissions from propellant release during the use of our pMDI inhalers account for 73% of our total scope 3 emissions. Propellant HFA-134a has a GWP of approximately 1300 therefore GHG emissions linked to the use of this gas are significant. Propellant release during use of pMDI inhalers can't be avoided, being necessary to properly deliver the drug to the patient.To reduce these emissions Chiesi Group has a 5-year, €350 million investment plan to bring new pMDI formulations to market using a new propellant with low GWP (equal to 138), which will reduce the carbon footprint of pMDI inhalers by 90%. Transition to this new formulation requires development and approval from regulatory bodies and Chiesi has developed an ambitious timeline for this transition. At the same time, Chiesi continues to invest in dry powder inhaler (DPI) technology that does not contain propellants and so has a much lower carbon footprint relative to pMDI technology, in order to provide patients with the whole range of therapies they need.Additionally, until pMDIs with new propellant become available, Chiesi is promoting and endorsing virtuous behaviours aimed at optimizing inhalation therapy (to reduce unnecessary emissions) and is investing in disposal/recycling schemes to enable capture of left-over propellant in used inhalers.\"}\n",
"================================================================================\n",
"Validation Data\n",
"{'text': 'SBT targets were set and approved for three businesses of the company namely MLDL, MWCDL (MWC Chennai), and MWCJL (MWC Jaipur) in 2019. The Base Year for MLDL and MWCJL was considered as 2018, as the complete GHG inventory for all the three businesses was recalculated in 2018 (i.e., FY 19) as part of SBTi; whereas the Base Year for MWCDL was considered as 2016, since the most comprehensive GHG inventory data was available for the same.Reason for recalculation of GHG Inventory:Earlier, for Residential Business i.e.; MLDL, we used to consider the emissions from fuel consumption by our Project sites and Contractors under Scope 1; but with detailed inventorisation in FY 19 as part of SBTi, the emissions from fuel consumption by Contractors alone were categorized under Scope 3 as per the GHG protocol, and the emissions from fuel consumption by our Project sites alone were considered under Scope 1. Similarly, the emissions from Electricity consumption for our Project sites and Contractors were considered under Scope 2; but as part of SBTi, the emissions from Electricity consumption by Contractors and Project sites were separately considered under Scope 3, and Scope 2 respectively. The same process was followed for MWCJL. Segregation of the Fuel and Electricity Consumption by Project sites and Contractors was not possible for 2016 (i.e., FY 17) due to unavailability of separate data for sites and contractors; hence the base year for MLDL and MWCJL was considered as 2018, as the complete GHG inventory was recalculated for FY 19 for the two business divisions - MLDL and MWCJL; whereas for MWCDL the Base Year was taken as 2016 as the complete inventory was available for the same. The target as mentioned in this section, is target set for MWCDL. The Scope 1 & 2 emissions for MWCDL constituted about 100% of the total Scope 1 & 2 emission of the three business divisions in the base year (2016) as only MWCDL was with base year of 2016. Please find the Scope 1 & 2 emissions calculated for the three businesses namely MLDL, MWCDL, and MWCJL in the Base Year (2018) and for MWCDL in 2016 as mentioned. For MLDL and MWCJL - Base Year (2018) – MLDL: Scope 1 - 47 tCO2e and Scope 2 - 315 tCO2e | MWCJL: Scope 1 - 97 tCO2e and Scope 2 - 845 tCO2e |For MWCDL - Base Year (2016) – MWCDL: Scope 1 - 244 tCO2e and Scope 2 - 2161 tCO2e'}\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"df_Mask_test = df_Mask[\"test\"]\n",
"df_Mask_test[0]\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lAWncEnbmrMP",
"outputId": "06f5dcf9-d314-4c9b-b3f6-2512d934cad7"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'text': \"Emissions from propellant release during the use of our pMDI inhalers account for 73% of our total scope 3 emissions. Propellant HFA-134a has a GWP of approximately 1300 therefore GHG emissions linked to the use of this gas are significant. Propellant release during use of pMDI inhalers can't be avoided, being necessary to properly deliver the drug to the patient.To reduce these emissions Chiesi Group has a 5-year, €350 million investment plan to bring new pMDI formulations to market using a new propellant with low GWP (equal to 138), which will reduce the carbon footprint of pMDI inhalers by 90%. Transition to this new formulation requires development and approval from regulatory bodies and Chiesi has developed an ambitious timeline for this transition. At the same time, Chiesi continues to invest in dry powder inhaler (DPI) technology that does not contain propellants and so has a much lower carbon footprint relative to pMDI technology, in order to provide patients with the whole range of therapies they need.Additionally, until pMDIs with new propellant become available, Chiesi is promoting and endorsing virtuous behaviours aimed at optimizing inhalation therapy (to reduce unnecessary emissions) and is investing in disposal/recycling schemes to enable capture of left-over propellant in used inhalers.\"}"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"df_Mask['train'].features"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fUjSIfkbnFNv",
"outputId": "8e8b3122-519a-4b91-9fc4-e809f32c5977"
},
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'text': Value(dtype='string', id=None)}"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"df_Mask.keys()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2GARA2LqMEti",
"outputId": "a776f68b-b6a8-4a00-faf6-fa3dfba302c9"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict_keys(['train', 'validation', 'test'])"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"source": [
"TOKENIZATION"
],
"metadata": {
"id": "utisidlid-lM"
}
},
{
"cell_type": "code",
"source": [
"checkpoint = \"climatebert/distilroberta-base-climate-f\"\n",
"tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
"\n",
"\n",
"\n",
"def tokenize_function(df_Mask):\n",
" return tokenizer(df_Mask[\"text\"], df_Mask['text'], df_Mask['text'], truncation=True, return_tensors='pt', padding=\"max_length\")\n",
"\n",
"tokenized_datasets = df_Mask.map(tokenize_function, batched=True)\n",
"data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
"tokenized_datasets"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 478,
"referenced_widgets": [
"6138d96b242a4b80b0d19a5806493fc1",
"c9dff572c96e453ea197434240067535",
"6acf85b19d994939b89ed153b1775d4f",
"bd682c078e44455192f300e01db6a8aa",
"d0ee1d2d88e242aeb258faa1cff7dac8",
"8435a8628aca4773b8a8a504121892e6",
"9e5db75046b243aa89a1fa2615cabf4a",
"6009f597c58145d6b61b4a76c8112830",
"123221f6358f4c468856e334cc909ff0",
"2e7905c68fff40219f2dbf0107ef591a",
"c323d5be284d46c8b10783ecd7021327",
"fb726d8383e9458f95e5f45a4ba7ad39",
"92ac34f24fe4487b9ca71e0201fccb0e",
"aff6a0b65f2c4a7b9cb3608c7748c8ca",
"52da811b5b6e4667938ef2810d63336e",
"853774c9869e49e3af5f3c2ea524a647",
"0dbbc8e5be7e48dc9ec940502f24f035",
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"7eeb2eadfbf94aeaa9604870d37cc2d5",
"11d0cfd6d00449f2945c111f4f9e4b9c",
"006c8566ed7b46fcba7670eedb23375e",
"01cf8a86a6884082a2c2fb9b987df3a0",
"e076887b4fbe40318e2d5ab74d00ed6d",
"89409618c1164997b33f4b1da60821b9",
"cc5090c3a912435e9dd664ee1410d4f5",
"ede566c591ef475391ab2f6541475ad7",
"3c52aabbf17649f79aa61b269e8c7059",
"113478fb0c0b48559d6a0161d8b19e81",
"326b6c94d2b044eba94da023b65d25d6",
"18fafc0849dd41bb8f02ba72769e39b1",
"d78727e483cb4b2c8b1e7e941341296e",
"f7bcf293701f46479015a05dbaf5cf09",
"15f03e9c9a4f4aa39f33840ce22711a4"
]
},
"id": "315bZGrUpG9y",
"outputId": "7522e098-487e-46d8-9f0c-944bc4fb476e"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"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",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n",
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/2646 [00:00) torch.Size([8, 512, 50500])\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from transformers import AdamW\n",
"\n",
"# Our Optimizer\n",
"optimizer = AdamW(model_Mask.parameters(), lr=5e-5)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Jg0E_vxZumlY",
"outputId": "1ea23670-0cf3-41db-d4d7-978f7b16e5e0"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: 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",
" warnings.warn(\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Learning Rate Scheduler"
],
"metadata": {
"id": "LOrbHy55nkFQ"
}
},
{
"cell_type": "code",
"source": [
"from transformers import get_scheduler\n",
"\n",
"num_epochs = 1\n",
"num_training_steps = num_epochs * len(train_dataloader)\n",
"lr_scheduler = get_scheduler(\n",
" \"linear\",\n",
" optimizer=optimizer,\n",
" num_warmup_steps=0,\n",
" num_training_steps=num_training_steps,\n",
")\n",
"print(num_training_steps)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mtqN64_xnnLt",
"outputId": "7f3f96b4-23af-41e5-a512-b2f760aa71f0"
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"331\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Defining and Pushing the model to our Device"
],
"metadata": {
"id": "C04j2xDMnrgi"
}
},
{
"cell_type": "code",
"source": [
"import torch\n",
"\n",
"device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
"model_Mask.to(device)\n",
"print(\"========================================================================================\")\n",
"print(\"Our available device which our model is going to be trained on is: \\n\\n\", device)\n",
"print(\"========================================================================================\")\n",
"# Making sure our model is ready to be trained\n",
"print(\"Our Training Model architecture is: \\n\\n\", model_Mask.train())\n",
"print(\"========================================================================================\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KC-RcC3snsr7",
"outputId": "a7777419-feb9-4aed-f5d7-26e5b480aa58"
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"========================================================================================\n",
"Our available device which our model is going to be trained on is: \n",
"\n",
" cuda\n",
"========================================================================================\n",
"Our Training Model architecture is: \n",
"\n",
" RobertaForMaskedLM(\n",
" (roberta): RobertaModel(\n",
" (embeddings): RobertaEmbeddings(\n",
" (word_embeddings): Embedding(50500, 768, padding_idx=1)\n",
" (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
" (token_type_embeddings): Embedding(1, 768)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (encoder): RobertaEncoder(\n",
" (layer): ModuleList(\n",
" (0-5): 6 x RobertaLayer(\n",
" (attention): RobertaAttention(\n",
" (self): RobertaSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): RobertaSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): RobertaIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" (intermediate_act_fn): GELUActivation()\n",
" )\n",
" (output): RobertaOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (lm_head): RobertaLMHead(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (decoder): Linear(in_features=768, out_features=50500, bias=True)\n",
" )\n",
")\n",
"========================================================================================\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Fine Tuning"
],
"metadata": {
"id": "trnweQQFpykI"
}
},
{
"cell_type": "code",
"source": [
"from transformers import AdamW\n",
"\n",
"# Our Optimizer\n",
"optimizer = AdamW(model_Mask.parameters(), lr=5e-5)\n",
"\n",
"\n",
"\n"
],
"metadata": {
"id": "Ll0Amnndp0E1"
},
"execution_count": 24,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Making Use of an Accelerator"
],
"metadata": {
"id": "ofZZpBQxzWVH"
}
},
{
"cell_type": "code",
"source": [
"from accelerate import Accelerator\n",
"\n",
"accelerator = Accelerator()\n",
"\n",
"\n",
"train_dataloader, eval_dataloader, model_Mask, optimizer= accelerator.prepare(\n",
" train_dataloader,\n",
" eval_dataloader,\n",
" model_Mask,\n",
" optimizer\n",
" )"
],
"metadata": {
"id": "sNQPiMhozZw1"
},
"execution_count": 25,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Training"
],
"metadata": {
"id": "cS7t4zZzq_d2"
}
},
{
"cell_type": "code",
"source": [
"from tqdm.auto import tqdm\n",
"\n",
"\n",
"# function to train the model\n",
"def train(\n",
" model=model_Mask,\n",
" train_dataloader=train_dataloader,\n",
" optimizer=optimizer,\n",
" # cross_entropy=cross_entropy,\n",
" device=device,\n",
" eval_dataset=eval_dataloader\n",
"):\n",
"\n",
" model_Mask.train()\n",
"\n",
"progress_bar = tqdm(range(num_training_steps))\n",
"\n",
"for epoch in range(num_epochs):\n",
" for batch in train_dataloader:\n",
" outputs = model_Mask(**batch)\n",
" loss = outputs.loss\n",
" accelerator.backward(loss)\n",
"\n",
" optimizer.step()\n",
" lr_scheduler.step()\n",
" optimizer.zero_grad()\n",
" progress_bar.update(1)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"a95054b04eab4909b59f7997dfe5c4ee",
"277f0af87a014fdd816aa9fd96feaf15",
"3350a028bf0149c4b7d81e18c1708083",
"4fcb555685da4696a0c96917c9f3aa41",
"c3d3266b01fe4e3080c0078c847cfc08",
"d65d25d36a55459f93a0fe38a180a593",
"95253e82208946e1865ccf8cb3802173",
"3f24bfa2596644e8b0a381559dd160a2",
"a0772717c1bd451e9acc306051256d2a",
"d5ac149582d14e4486ef45b825199fc8",
"a7454532287e487887c47c132d3787db"
]
},
"id": "oMx2PZLJrAiB",
"outputId": "8871ccec-81ca-4184-9b36-6c0d7f990ec6"
},
"execution_count": 26,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/331 [00:00