{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "ename": "", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31mRunning cells with 'Python 3.12.3' requires the ipykernel package.\n", "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n", "\u001b[1;31mCommand: '/usr/bin/python3 -m pip install ipykernel -U --user --force-reinstall'" ] } ], "source": [ "import spacy\n", "\n", "# Load the English NLP model\n", "nlp = spacy.load(\"en_core_web_sm\")\n", "\n", "def extract_item_name(sentence):\n", " doc = nlp(sentence)\n", " possible_items = []\n", "\n", " for chunk in doc.noun_chunks:\n", " filtered_tokens = [token.text for token in chunk if not (token.pos_ in [\"DET\", \"NUM\"])]\n", "\n", " if any(tok.pos_ in [\"NOUN\", \"PROPN\"] for tok in chunk):\n", " possible_items.append(\" \".join(filtered_tokens))\n", "\n", " if possible_items:\n", " return max(possible_items, key=len)\n", "\n", " return None\n", "\n", "# Example usage\n", "sentence = \"Can I get one Chicken Katsu Curry with extra rice?\"\n", "item_name = extract_item_name(sentence)\n", "print(f\"Extracted item name: {item_name}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "PnK_4azs5LBd" }, "source": [ "# **1. Installation of Required Libraries**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dAq-cgrU5QRJ", "outputId": "58e436b6-734e-4dd5-85d0-71e559ccaedd" }, "outputs": [], "source": [ "# %pip install transformers datasets torch scikit-learn -q\n", "# %pip install transformers[torch] -q\n", "# %pip install accelerate -U -q" ] }, { "cell_type": "markdown", "metadata": { "id": "Gef1it6r5dbW" }, "source": [ "# **2. Implementation Code**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "q7lTlKG55Wmz" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "import torch\n", "from datasets import load_dataset\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n", "from sklearn.metrics import accuracy_score, precision_recall_fscore_support" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.model_selection import train_test_split\n", "import torch\n", "from torch import nn\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "dataset = pd.read_excel(\"fine_tune_dataset.xlsx\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SentenceOperationItem NameQuantity
0Add 3 bananas to my cart.addbananas3
1Can you put 5 oranges in the cart?addoranges5
2Please remove 1 pack of cookies from the cart.removecookies1
3I need to update the cart with 4 bottles of milk.updatemilk4
4Take 2 bags of chips out of my cart.removechips2
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" ], "text/plain": [ " Sentence Operation Item Name \\\n", "0 Add 3 bananas to my cart. add bananas \n", "1 Can you put 5 oranges in the cart? add oranges \n", "2 Please remove 1 pack of cookies from the cart. remove cookies \n", "3 I need to update the cart with 4 bottles of milk. update milk \n", "4 Take 2 bags of chips out of my cart. remove chips \n", "\n", " Quantity \n", "0 3 \n", "1 5 \n", "2 1 \n", "3 4 \n", "4 2 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.head(5)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 473, "referenced_widgets": [ "f7e274267c304182875fc0dbcc1cfcef", "01684c31164f4492b071a33a95600d9b", "e4203bd3c36944f198bc55e0ea24df24", "46cfc36544844967baae34b374bb1bfd", "40b56c0545774429a82cfffe5f4feb21", "3f35a887fb764256ac34b1ecd7fc41ae", "8172756bc2da40bbb7f81d7a6f36699f", "a50648375b6343139e5f0532bbebcd85", 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"c1aca84324e744a1a6d9813c73e316c9", "88943d24e2444973af7fee4cb83c108d", "ede9af7988b744afbcde85c9dbcb16b6", "a9501721caf94fcfa8648d163c6b63d2", "aacfacc91fc246f59baa8ec965410efd" ] }, "id": "PGoLzOa15u3b", "outputId": "89654562-f5c9-42f5-86ef-b6db95043edf" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:1617: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be deprecated in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n", " warnings.warn(\n" ] } ], "source": [ "# dataset = load_dataset(\"imdb\")\n", "model_name = \"distilbert-base-uncased\"\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "AahEP4v2HtaW" }, "outputs": [], "source": [ "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "device = torch.device(\"mps\" if torch.backends.mps.is_available() else \"cpu\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "device(type='cpu')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "device" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "504c5b0df15e429db4aa4b24502df830", "92efa50f401b4358aaa7c0123bb3d888", "b65efca5f73d49c88a27423bfaf620e2", "34b758995227462d986887694b846c26", "42de283d319740778d9fb1816c9eb345", "c5e194fcf6bc4f4cb7ac1f441089ff2e", "078ec68222354cd3a074b22559b6e629", "da17f10f53434952b3e641ab05970eff", "afe594b718bc41d298d94b7d0de662db", "840f6918211141dbb35c7155a47a2f1e", "d0df2b4085944b57b31c698fa4551264", "e569e3698f58422a8d4a6d0f7fbe5709", "1fc0a3c731e54341a88b93d92f46796f", "10c9e51cf67541959d81040c01715452", "dc21bd8c6a23484dba4e039ce48e9ab5", "e8a5e104151a41fd94b8ea981490154b", "43f1f28576af458ab2d82176ffbafb86", "59e7c93af8fc4c01b1aa382a1627fb29", "be45e8df21384663b7f4abed6df00baa", "ae1bf853bdd14cb7ae2b5b4a449b1853", "230becb0d6bf451e8909b3720abfea8a", "c8e208e13f244e66927507d490dfc90a", "9884c1ce75944357af8fa26020fcaa79", "7a3f0ee287c24466864fc03ac646956c", "fe058673d48b4f388700e71de5b99162", "8defa4b5de83427ca22b9fb91b84fb14", "df5b8c9af1cf4f74a43a91016936f80c", "e6e1b11d89074967bd7d4c0449ab1685", "3fcf0698c174408fb5dacbf392bbf29b", "75a1f1fda2b849548a4a01f1d8639564", "76dedbc86d69405b90cd497cedf0089c", "09304d2544f146b28a48d14511ca20de", "7f7cef22cec640db839159b2aa20cb45" ] }, "id": "UZs355r-52fp", "outputId": "3b88332e-0877-4ea5-f834-bcdcd5b42b00" }, "outputs": [], "source": [ "dataset['tokenized'] = dataset['Sentence'].apply(lambda x: tokenizer(x, padding='max_length', truncation=True, return_tensors=\"pt\"))\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SentenceOperationItem NameQuantitytokenized
644What is currently in my shopping cart?viewnonenone[input_ids, attention_mask]
645Please show my cart.viewnonenone[input_ids, attention_mask]
646Can I see the current contents of my cart?viewnonenone[input_ids, attention_mask]
647Could you show me my cart status?viewnonenone[input_ids, attention_mask]
648What are the items in my cart right now?viewnonenone[input_ids, attention_mask]
\n", "
" ], "text/plain": [ " Sentence Operation Item Name Quantity \\\n", "644 What is currently in my shopping cart? view none none \n", "645 Please show my cart. view none none \n", "646 Can I see the current contents of my cart? view none none \n", "647 Could you show me my cart status? view none none \n", "648 What are the items in my cart right now? view none none \n", "\n", " tokenized \n", "644 [input_ids, attention_mask] \n", "645 [input_ids, attention_mask] \n", "646 [input_ids, attention_mask] \n", "647 [input_ids, attention_mask] \n", "648 [input_ids, attention_mask] " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.tail(5)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "operation_mapping = {'add': 0, 'remove': 1, 'update': 2, 'view': 3}\n", "dataset['Operation_Label'] = dataset['Operation'].map(operation_mapping)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "dbWw2dD-58Ky" }, "outputs": [], "source": [ "train_operation_df, test_operation_df = train_test_split(dataset[['Sentence', 'Operation']], test_size=0.2, random_state=42)\n", "\n", "train_quantity_df, test_quantity_df = train_test_split(dataset[['Sentence', 'Quantity']], test_size=0.2, random_state=42)\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "N7caBVWU59i0", "outputId": "e958a3e8-1c8c-45f5-c24e-0808e5a6e9a8" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] }, { "data": { "text/plain": [ "DistilBertForSequenceClassification(\n", " (distilbert): DistilBertModel(\n", " (embeddings): Embeddings(\n", " (word_embeddings): Embedding(30522, 768, padding_idx=0)\n", " (position_embeddings): Embedding(512, 768)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (transformer): Transformer(\n", " (layer): ModuleList(\n", " (0-5): 6 x TransformerBlock(\n", " (attention): MultiHeadSelfAttention(\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (q_lin): Linear(in_features=768, out_features=768, bias=True)\n", " (k_lin): Linear(in_features=768, out_features=768, bias=True)\n", " (v_lin): Linear(in_features=768, out_features=768, bias=True)\n", " (out_lin): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (ffn): FFN(\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (lin1): Linear(in_features=768, out_features=3072, bias=True)\n", " (lin2): Linear(in_features=3072, out_features=768, bias=True)\n", " (activation): GELUActivation()\n", " )\n", " (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " )\n", " )\n", " )\n", " (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n", " (classifier): Linear(in_features=768, out_features=20, bias=True)\n", " (dropout): Dropout(p=0.2, inplace=False)\n", ")" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Operation classification model (3 labels: add, remove, update)\n", "operation_model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(operation_mapping))\n", "operation_model.to(device)\n", "\n", "# Quantity classification model (for simplicity, assuming up to 20 distinct quantities)\n", "quantity_model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=20)\n", "quantity_model.to(device)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "u6FezLZY6IZz", "outputId": "70232730-d898-4d4b-c0b5-0742441541f1" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of πŸ€— Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n" ] } ], "source": [ "from transformers import Trainer, TrainingArguments\n", "\n", "# Training arguments\n", "training_args = TrainingArguments(\n", " output_dir=\"./results\",\n", " evaluation_strategy=\"epoch\",\n", " save_strategy=\"epoch\",\n", " learning_rate=2e-5,\n", " per_device_train_batch_size=16,\n", " per_device_eval_batch_size=16,\n", " num_train_epochs=10,\n", " weight_decay=0.01,\n", " logging_dir='./logs',\n", " logging_steps=10,\n", " load_best_model_at_end=True,\n", " save_total_limit=2,\n", " save_steps=500,\n", ")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score, precision_recall_fscore_support" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "ACVm1fs46JTj" }, "outputs": [], "source": [ "def compute_metrics(pred):\n", " labels = pred.label_ids\n", " preds = pred.predictions.argmax(-1)\n", " precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='weighted')\n", " acc = accuracy_score(labels, preds)\n", " return {\n", " 'accuracy': acc,\n", " 'f1': f1,\n", " 'precision': precision,\n", " 'recall': recall\n", " }" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Define the mapping from operation strings to integer labels\n", "operation_label_mapping = {\n", " \"add\": 0,\n", " \"remove\": 1,\n", " \"update\": 2,\n", " \"view\": 3\n", "}\n", "\n", "# Apply the mapping to the dataset\n", "train_operation_df[\"Operation_Label\"] = train_operation_df[\"Operation\"].map(operation_label_mapping)\n", "test_operation_df[\"Operation_Label\"] = test_operation_df[\"Operation\"].map(operation_label_mapping)\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "class CustomDataset(torch.utils.data.Dataset):\n", " def __init__(self, df, tokenizer, max_length=128):\n", " self.df = df\n", " self.tokenizer = tokenizer\n", " self.max_length = max_length\n", "\n", " def __len__(self):\n", " return len(self.df)\n", "\n", " def __getitem__(self, idx):\n", " # Ensure the idx is within the correct range\n", " if torch.is_tensor(idx):\n", " idx = idx.tolist()\n", "\n", " # Fetch the row by index\n", " sentence = self.df.iloc[idx][\"Sentence\"]\n", " operation_label = self.df.iloc[idx][\"Operation_Label\"] # Use the integer label now\n", "\n", " # Tokenize the sentence\n", " encoding = self.tokenizer(\n", " sentence,\n", " padding=\"max_length\",\n", " truncation=True,\n", " max_length=self.max_length,\n", " return_tensors=\"pt\"\n", " )\n", "\n", " # Create a dictionary with input data and label\n", " item = {\n", " \"input_ids\": encoding[\"input_ids\"].squeeze(),\n", " \"attention_mask\": encoding[\"attention_mask\"].squeeze(),\n", " \"labels\": torch.tensor(operation_label, dtype=torch.long) # Use the integer label\n", " }\n", "\n", " return item\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "train_dataset = CustomDataset(train_operation_df, tokenizer)\n", "test_dataset = CustomDataset(test_operation_df, tokenizer)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "yT6a4s8f6L5s" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 3%|β–Ž | 10/330 [02:19<1:15:04, 14.08s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'loss': 1.3225, 'grad_norm': 2.9805526733398438, 'learning_rate': 1.9393939393939395e-05, 'epoch': 0.3}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 6%|β–Œ | 20/330 [05:32<1:51:00, 21.49s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'loss': 1.0513, 'grad_norm': 4.0864691734313965, 'learning_rate': 1.8787878787878792e-05, 'epoch': 0.61}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 9%|β–‰ | 30/330 [09:31<2:03:13, 24.65s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'loss': 0.7933, 'grad_norm': 2.4576473236083984, 'learning_rate': 1.8181818181818182e-05, 'epoch': 0.91}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 10%|β–ˆ | 33/330 [10:43<1:54:46, 23.19s/it]/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. 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26.72s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'train_runtime': 8817.7112, 'train_samples_per_second': 0.589, 'train_steps_per_second': 0.037, 'train_loss': 0.15002734196005446, 'epoch': 10.0}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 9/9 [00:21<00:00, 2.40s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Evaluation Results: {'eval_loss': 0.007898399606347084, 'eval_accuracy': 1.0, 'eval_f1': 1.0, 'eval_precision': 1.0, 'eval_recall': 1.0, 'eval_runtime': 24.4138, 'eval_samples_per_second': 5.325, 'eval_steps_per_second': 0.369, 'epoch': 10.0}\n" ] }, { "data": { "text/plain": [ "('./fine-tuned-operation-model/tokenizer_config.json',\n", " './fine-tuned-operation-model/special_tokens_map.json',\n", " './fine-tuned-operation-model/vocab.txt',\n", " './fine-tuned-operation-model/added_tokens.json',\n", " './fine-tuned-operation-model/tokenizer.json')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "operation_trainer = Trainer(\n", " model=operation_model,\n", " args=training_args,\n", " train_dataset=train_dataset,\n", " eval_dataset=test_dataset,\n", " compute_metrics=compute_metrics\n", ")\n", "\n", "# Train the model\n", "operation_trainer.train()\n", "\n", "# Evaluate the model\n", "eval_results = operation_trainer.evaluate()\n", "print(f\"Evaluation Results: {eval_results}\")\n", "\n", "# Save the model\n", "operation_model.save_pretrained(\"./fine-tuned-operation-model\")\n", "tokenizer.save_pretrained(\"./fine-tuned-operation-model\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "import torch\n", "from torch.utils.data import Dataset\n", "\n", "class QuantityDataset(Dataset):\n", " def __init__(self, df, tokenizer, max_length=128):\n", " self.df = df\n", " self.tokenizer = tokenizer\n", " self.max_length = max_length\n", "\n", " def __len__(self):\n", " return len(self.df)\n", "\n", " def __getitem__(self, idx):\n", " # Ensure the idx is within the correct range\n", " if torch.is_tensor(idx):\n", " idx = idx.tolist()\n", "\n", " # Fetch the row by index\n", " sentence = self.df.iloc[idx][\"Sentence\"]\n", " quantity_label = self.df.iloc[idx][\"Quantity\"] # Use the integer label now\n", "\n", " # Tokenize the sentence\n", " encoding = self.tokenizer(\n", " sentence,\n", " padding=\"max_length\",\n", " truncation=True,\n", " max_length=self.max_length,\n", " return_tensors=\"pt\"\n", " )\n", "\n", " # Create a dictionary with input data and label\n", " item = {\n", " \"input_ids\": encoding[\"input_ids\"].squeeze(),\n", " \"attention_mask\": encoding[\"attention_mask\"].squeeze(),\n", " \"labels\": torch.tensor(quantity_label, dtype=torch.long) # Use the integer label\n", " }\n", "\n", " return item\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Replace NaN in 'quantity' column with 0\n", "train_quantity_df['Quantity'] = pd.to_numeric(train_quantity_df['Quantity'], errors='coerce').fillna(0)\n", "test_quantity_df['Quantity'] = pd.to_numeric(test_quantity_df['Quantity'], errors='coerce').fillna(0)\n", "\n", "# train_quantity_df[\"Quantity\"] = train_quantity_df['Quantity'].fillna(0)\n", "# test_quantity_df[\"Quantity\"] = test_quantity_df['Quantity'].fillna(0)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "train_quantity_df[\"Quantity\"] = train_quantity_df[\"Quantity\"].astype(int)\n", "test_quantity_df[\"Quantity\"] = test_quantity_df[\"Quantity\"].astype(int)\n", "\n", "# Create a CustomDataset for the quantity model\n", "train_quantity_dataset = QuantityDataset(train_quantity_df, tokenizer)\n", "test_quantity_dataset = QuantityDataset(test_quantity_df, tokenizer)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 3%|β–Ž | 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10.0}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 330/330 [1:26:17<00:00, 15.69s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'train_runtime': 5177.7572, 'train_samples_per_second': 1.002, 'train_steps_per_second': 0.064, 'train_loss': 1.0024475039857808, 'epoch': 10.0}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 9/9 [00:34<00:00, 3.79s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Evaluation Results for Quantity Model: {'eval_loss': 0.17457057535648346, 'eval_accuracy': 1.0, 'eval_f1': 1.0, 'eval_precision': 1.0, 'eval_recall': 1.0, 'eval_runtime': 38.991, 'eval_samples_per_second': 3.334, 'eval_steps_per_second': 0.231, 'epoch': 10.0}\n" ] }, { "data": { "text/plain": [ "('./fine-tuned-quantity-model/tokenizer_config.json',\n", " './fine-tuned-quantity-model/special_tokens_map.json',\n", " './fine-tuned-quantity-model/vocab.txt',\n", " './fine-tuned-quantity-model/added_tokens.json',\n", " './fine-tuned-quantity-model/tokenizer.json')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quantity_trainer = Trainer(\n", " model=quantity_model, # The model for quantities (numeric prediction)\n", " args=training_args,\n", " train_dataset=train_quantity_dataset, # Training dataset for quantity\n", " eval_dataset=test_quantity_dataset, # Evaluation dataset for quantity\n", " compute_metrics=compute_metrics\n", ")\n", "\n", "# Train the quantity model\n", "quantity_trainer.train()\n", "\n", "# Evaluate the quantity model on the test dataset\n", "eval_results_quantity = quantity_trainer.evaluate(eval_dataset=test_quantity_dataset)\n", "print(f\"Evaluation Results for Quantity Model: {eval_results_quantity}\")\n", "\n", "# Save the trained quantity model and tokenizer\n", "quantity_model.save_pretrained(\"./fine-tuned-quantity-model\")\n", "tokenizer.save_pretrained(\"./fine-tuned-quantity-model\")" ] }, { "cell_type": "markdown", "metadata": { "id": "m8KqgPo3Dhta" }, "source": [ "# **3. Using the Model for Prediction**\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import spacy\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n", "\n", "nlp = spacy.load(\"en_core_web_sm\")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xPJuvRerDyq3", "outputId": "87f5572c-97e4-4b6a-f07d-f43342f90ba7" }, "outputs": [ { "data": { "text/plain": [ "DistilBertForSequenceClassification(\n", " (distilbert): DistilBertModel(\n", " (embeddings): Embeddings(\n", " (word_embeddings): Embedding(30522, 768, padding_idx=0)\n", " (position_embeddings): Embedding(512, 768)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (transformer): Transformer(\n", " (layer): ModuleList(\n", " (0-5): 6 x TransformerBlock(\n", " (attention): MultiHeadSelfAttention(\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (q_lin): Linear(in_features=768, out_features=768, bias=True)\n", " (k_lin): Linear(in_features=768, out_features=768, bias=True)\n", " (v_lin): Linear(in_features=768, out_features=768, bias=True)\n", " (out_lin): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (ffn): FFN(\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (lin1): Linear(in_features=768, out_features=3072, bias=True)\n", " (lin2): Linear(in_features=3072, out_features=768, bias=True)\n", " (activation): GELUActivation()\n", " )\n", " (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " )\n", " )\n", " )\n", " (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n", " (classifier): Linear(in_features=768, out_features=20, bias=True)\n", " (dropout): Dropout(p=0.2, inplace=False)\n", ")" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "operation_model_name = \"./fine-tuned-operation-model\"\n", "quantity_model_name = \"./fine-tuned-quantity-model\"\n", "\n", "operation_tokenizer = AutoTokenizer.from_pretrained(operation_model_name)\n", "quantity_tokenizer = AutoTokenizer.from_pretrained(quantity_model_name)\n", "\n", "operation_model = AutoModelForSequenceClassification.from_pretrained(operation_model_name)\n", "quantity_model = AutoModelForSequenceClassification.from_pretrained(quantity_model_name)\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "operation_model.to(device)\n", "quantity_model.to(device)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "id": "PvzjdLk0Dm1W" }, "outputs": [], "source": [ "def predict_operation(sentence):\n", " inputs = operation_tokenizer(sentence, padding=True, truncation=True, return_tensors=\"pt\").to(device)\n", " with torch.no_grad():\n", " outputs = operation_model(**inputs)\n", " logits = outputs.logits\n", " predictions = torch.softmax(logits, dim=-1)\n", " predicted_label = torch.argmax(predictions, dim=-1).item()\n", " operation = \"Add\" if predicted_label == 0 else \"Remove\" # Adjust based on your label encoding\n", " return operation, predictions[0].cpu().numpy()\n", "\n", "def predict_quantity(sentence):\n", " inputs = quantity_tokenizer(sentence, padding=True, truncation=True, return_tensors=\"pt\").to(device)\n", " with torch.no_grad():\n", " outputs = quantity_model(**inputs)\n", " logits = outputs.logits\n", " predictions = torch.softmax(logits, dim=-1)\n", " predicted_quantity = torch.argmax(predictions, dim=-1).item()\n", " return predicted_quantity, predictions[0].cpu().numpy()\n", "\n", "def extract_item_name(sentence):\n", " doc = nlp(sentence)\n", " noun_chunks = [chunk.text for chunk in doc.noun_chunks]\n", " for chunk in noun_chunks:\n", " if any(tok.pos_ == \"NOUN\" for tok in nlp(chunk).doc):\n", " return chunk\n", "\n", " return None" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BjgV2dU1DsMk", "outputId": "5651b853-9c6a-4ca9-a289-8cc154416d00" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sentence: Add soups to my cart.\n", "Predicted Operation: Add\n", "Operation Probabilities: [0.9962047 0.00111221 0.00120974 0.00147345]\n", "Predicted Quantity: 1\n", "Quantity Probabilities: [0.07971817 0.2707245 0.1223022 0.11824853 0.03780839 0.09126952\n", " 0.06610113 0.05348995 0.03518798 0.02410132 0.02650298 0.00751828\n", " 0.01491447 0.00636614 0.00600904 0.00775851 0.00745718 0.00924029\n", " 0.00832553 0.00695582]\n", "Item Name -----> soups\n" ] } ], "source": [ "sentence = \"Add soups to my cart.\"\n", "\n", "# Predict operation and quantity\n", "operation, operation_probs = predict_operation(sentence)\n", "quantity, quantity_probs = predict_quantity(sentence)\n", "\n", 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