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"Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
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]
}
],
"source": [
"pip install transformers datasets huggingface_hub sentence-transformers"
]
},
{
"cell_type": "code",
"source": [
"import re\n",
"import nltk\n",
"from nltk.corpus import stopwords\n",
"import torch\n",
"from torch.utils.data import DataLoader, TensorDataset\n",
"from transformers import AutoTokenizer, AutoModelForMaskedLM, AdamW\n",
"import pandas as pd\n",
"from tqdm import tqdm"
],
"metadata": {
"id": "Jk533_F14yV8"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Load your unlabeled dataset\n",
"resumes = pd.read_csv('/content/resumes6000.csv')"
],
"metadata": {
"id": "IR-KIxHd5iyu"
},
"execution_count": 23,
"outputs": []
},
{
"cell_type": "code",
"source": [
"resumes.head(5)"
],
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" Resumes\n",
"0 Global Sales Administrator Biamp Systems Globa...\n",
"1 Python Developer - Sprint 8 years of experien...\n",
"2 IT Project Manager - Scrum Master of Digital ...\n",
"3 UI Front End Developer UI <span class=\"hl\">Fro...\n",
"4 IT Security Analyst Camp Hill, PA Work Experie..."
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},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"source": [
"# Define the function for cleaning text\n",
"def clean_text(text):\n",
" return re.sub(r\"<span class=\\\"hl\\\">(.*?)</span>\", r\"\\1\", text)\n",
"# Apply the function to the entire column\n",
"resumes['Resumes'] = resumes['Resumes'].apply(clean_text)"
],
"metadata": {
"id": "MrCrvWv65nAw"
},
"execution_count": 26,
"outputs": []
},
{
"cell_type": "code",
"source": [
" import nltk\n",
" nltk.download('punkt')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aUdNZquW4yXo",
"outputId": "254067bd-9b4e-4e98-b8a0-9c661e6955f3"
},
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"source": [
"import nltk\n",
"nltk.download('stopwords')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "09C8uhGu51Vh",
"outputId": "3cd7a9af-293f-4c3c-a073-92fe26c49bd5"
},
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"source": [
"# Function for cleaning and preprocessing the resume\n",
"def clean_resume(resume):\n",
" if isinstance(resume, str):\n",
" # Convert to lowercase\n",
" resume = resume.lower()\n",
"\n",
" # Remove URLs, RT, cc, hashtags, mentions, non-ASCII characters, punctuation, and extra whitespace\n",
" resume = re.sub('http\\S+\\s*|RT|cc|#\\S+|@\\S+|[^\\x00-\\x7f]|[^\\w\\s]', ' ', resume)\n",
" resume = re.sub('\\s+', ' ', resume).strip()\n",
"\n",
" # Tokenize the resume\n",
" tokens = nltk.word_tokenize(resume)\n",
"\n",
" # Remove stopwords\n",
" stop_words = set(stopwords.words('english'))\n",
" tokens = [token for token in tokens if token.lower() not in stop_words]\n",
"\n",
" # Join the tokens back into a sentence\n",
" preprocessed_resume = ' '.join(tokens)\n",
"\n",
" return preprocessed_resume\n",
" else:\n",
" return ''\n",
"# Applying the cleaning function to a Datasets\n",
"resumes['Resumes'] = resumes['Resumes'].apply(lambda x: clean_resume(x))"
],
"metadata": {
"id": "TWyPQ63w51kN"
},
"execution_count": 30,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"from transformers import AutoTokenizer, AutoModelForMaskedLM, AdamW\n",
"import torch\n",
"from torch.utils.data import DataLoader, TensorDataset\n",
"from tqdm import tqdm\n",
"\n",
"# Load the pre-trained model\n",
"mpnet = \"sentence-transformers/all-mpnet-base-v2\"\n",
"tokenizer = AutoTokenizer.from_pretrained(mpnet)\n",
"pretrained_model = AutoModelForMaskedLM.from_pretrained(mpnet)\n",
"\n",
"# Assuming 'resumes' is a DataFrame with a column named 'Resumes'\n",
"texts = resumes['Resumes'].tolist()\n",
"\n",
"# Tokenize and encode the unlabeled data\n",
"encodings = tokenizer(texts, padding=True, truncation = True, return_tensors='pt')\n",
"\n",
"# Create a TensorDataset\n",
"dataset = TensorDataset(encodings['input_ids'], encodings['attention_mask'])\n",
"\n",
"# Move the model to the appropriate device (CPU or GPU)\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"pretrained_model.to(device)\n",
"\n",
"# Initialize the optimizer\n",
"optimizer = AdamW(pretrained_model.parameters(), lr=2e-5)\n",
"\n",
"batch_size = 8\n",
"epochs = 3\n",
"import math\n",
"\n",
"# Experiment with different chunk sizes\n",
"chunk_sizes_to_try = [200] # Can add more sizes later\n",
"\n",
"for chunk_size in chunk_sizes_to_try:\n",
" for epoch in range(epochs):\n",
" tqdm_dataloader = tqdm(DataLoader(dataset, batch_size=batch_size, shuffle=True), desc=f'Epoch {epoch + 1}/{epochs}')\n",
"\n",
" pretrained_model.train()\n",
" for batch in tqdm_dataloader:\n",
" input_ids, attention_mask = batch\n",
" input_ids, attention_mask = input_ids.to(device), attention_mask.to(device)\n",
"\n",
" # Calculate number of chunks for current batch\n",
" sequence_length = input_ids.size(1) # Get actual sequence length\n",
" num_chunks = math.ceil(sequence_length / chunk_size)\n",
"\n",
" for i in range(num_chunks):\n",
" start_idx = i * chunk_size\n",
" end_idx = min((i + 1) * chunk_size, sequence_length) # Handle final chunk\n",
"\n",
" # Extract chunk data\n",
" input_ids_chunk = input_ids[:, start_idx:end_idx]\n",
" attention_mask_chunk = attention_mask[:, start_idx:end_idx]\n",
"\n",
" # Forward pass\n",
" outputs = pretrained_model(\n",
" input_ids_chunk, attention_mask=attention_mask_chunk, labels=input_ids_chunk.reshape(-1)\n",
" )\n",
"\n",
" # Calculate loss\n",
" loss = outputs.loss\n",
"\n",
" # Backward pass and optimization\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" # Update progress bar\n",
" tqdm_dataloader.set_postfix({'Loss': loss.item(), 'Chunk Size': chunk_size})"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kypmxXhz4ybO",
"outputId": "a142f965-498a-4f33-ffbb-028f88f27d51"
},
"execution_count": 43,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at sentence-transformers/all-mpnet-base-v2 were not used when initializing MPNetForMaskedLM: ['pooler.dense.weight', 'pooler.dense.bias']\n",
"- This IS expected if you are initializing MPNetForMaskedLM 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",
"- This IS NOT expected if you are initializing MPNetForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of MPNetForMaskedLM were not initialized from the model checkpoint at sentence-transformers/all-mpnet-base-v2 and are newly initialized: ['lm_head.dense.weight', 'lm_head.bias', 'lm_head.decoder.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
"/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",
"Epoch 1/3: 100%|██████████| 750/750 [11:46<00:00, 1.06it/s, Loss=0.057, Chunk Size=200]\n",
"Epoch 2/3: 100%|██████████| 750/750 [11:47<00:00, 1.06it/s, Loss=0.0571, Chunk Size=200]\n",
"Epoch 3/3: 100%|██████████| 750/750 [11:47<00:00, 1.06it/s, Loss=0.0464, Chunk Size=200]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Save the fine-tuned model\n",
"pretrained_model.save_pretrained('fine_tuned_mpnet')\n",
"tokenizer.save_pretrained('fine_tuned_mpnet')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "U-mZPfa8Sipl",
"outputId": "fc93a178-aaf4-415b-f8e2-bba93a832052"
},
"execution_count": 44,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('fine_tuned_mpnet/tokenizer_config.json',\n",
" 'fine_tuned_mpnet/special_tokens_map.json',\n",
" 'fine_tuned_mpnet/vocab.txt',\n",
" 'fine_tuned_mpnet/added_tokens.json',\n",
" 'fine_tuned_mpnet/tokenizer.json')"
]
},
"metadata": {},
"execution_count": 44
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "fnD7hsloTA1i"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "LEUEojrfTBB0"
},
"execution_count": null,
"outputs": []
}
]
} |