{ "cells": [ { "cell_type": "code", "source": [ "# πŸ› οΈ Step 1: Install required libraries quietly\n", "!pip install evaluate transformers datasets tokenizers seqeval pandas pyarrow -q\n", "\n", "# 🚫 Step 2: Disable Weights & Biases (WandB)\n", "import os\n", "os.environ[\"WANDB_MODE\"] = \"disabled\"\n", "\n", "# πŸ“š Step 2: Import necessary libraries\n", "import pandas as pd\n", "import datasets\n", "import numpy as np\n", "from transformers import BertTokenizerFast\n", "from transformers import DataCollatorForTokenClassification\n", "from transformers import AutoModelForTokenClassification\n", "from transformers import TrainingArguments, Trainer\n", "import evaluate\n", "from transformers import pipeline\n", "from collections import defaultdict\n", "import json\n", "\n", "# πŸ“₯ Step 3: Load the CoNLL-2025 NER dataset from Parquet\n", "# Download : https://huggingface.co/datasets/boltuix/conll2025-ner/blob/main/conll2025_ner.parquet\n", "parquet_file = \"conll2025_ner.parquet\"\n", "df = pd.read_parquet(parquet_file)\n", "\n", "# πŸ” Step 4: Convert pandas DataFrame to Hugging Face Dataset\n", "conll2025 = datasets.Dataset.from_pandas(df)\n", "\n", "# πŸ”Ž Step 5: Inspect the dataset structure\n", "print(\"Dataset structure:\", conll2025)\n", "print(\"Dataset features:\", conll2025.features)\n", "print(\"First example:\", conll2025[0])\n", "\n", "# 🏷️ Step 6: Extract unique tags and create mappings\n", "# Since ner_tags are strings, collect all unique tags\n", "all_tags = set()\n", "for example in conll2025:\n", " all_tags.update(example[\"ner_tags\"])\n", "unique_tags = sorted(list(all_tags)) # Sort for consistency\n", "num_tags = len(unique_tags)\n", "tag2id = {tag: i for i, tag in enumerate(unique_tags)}\n", "id2tag = {i: tag for i, tag in enumerate(unique_tags)}\n", "print(\"Number of unique tags:\", num_tags)\n", "print(\"Unique tags:\", unique_tags)\n", "\n", "# πŸ”§ Step 7: Convert string ner_tags to indices\n", "def convert_tags_to_ids(example):\n", " example[\"ner_tags\"] = [tag2id[tag] for tag in example[\"ner_tags\"]]\n", " return example\n", "\n", "conll2025 = conll2025.map(convert_tags_to_ids)\n", "\n", "# πŸ“Š Step 8: Split dataset based on 'split' column\n", "dataset_dict = {\n", " \"train\": conll2025.filter(lambda x: x[\"split\"] == \"train\"),\n", " \"validation\": conll2025.filter(lambda x: x[\"split\"] == \"validation\"),\n", " \"test\": conll2025.filter(lambda x: x[\"split\"] == \"test\")\n", "}\n", "conll2025 = datasets.DatasetDict(dataset_dict)\n", "print(\"Split dataset structure:\", conll2025)\n", "\n", "# πŸͺ™ Step 9: Initialize the tokenizer\n", "tokenizer = BertTokenizerFast.from_pretrained(\"boltuix/bert-mini\")\n", "\n", "# πŸ“ Step 10: Tokenize an example text and inspect\n", "example_text = conll2025[\"train\"][0]\n", "tokenized_input = tokenizer(example_text[\"tokens\"], is_split_into_words=True)\n", "tokens = tokenizer.convert_ids_to_tokens(tokenized_input[\"input_ids\"])\n", "word_ids = tokenized_input.word_ids()\n", "print(\"Word IDs:\", word_ids)\n", "print(\"Tokenized input:\", tokenized_input)\n", "print(\"Length of ner_tags vs input IDs:\", len(example_text[\"ner_tags\"]), len(tokenized_input[\"input_ids\"]))\n", "\n", "# πŸ”„ Step 11: Define function to tokenize and align labels\n", "def tokenize_and_align_labels(examples, label_all_tokens=True):\n", " \"\"\"\n", " Tokenize inputs and align labels for NER tasks.\n", "\n", " Args:\n", " examples (dict): Dictionary with tokens and ner_tags.\n", " label_all_tokens (bool): Whether to label all subword tokens.\n", "\n", " Returns:\n", " dict: Tokenized inputs with aligned labels.\n", " \"\"\"\n", " tokenized_inputs = tokenizer(examples[\"tokens\"], truncation=True, is_split_into_words=True)\n", " labels = []\n", " for i, label in enumerate(examples[\"ner_tags\"]):\n", " word_ids = tokenized_inputs.word_ids(batch_index=i)\n", " previous_word_idx = None\n", " label_ids = []\n", " for word_idx in word_ids:\n", " if word_idx is None:\n", " label_ids.append(-100) # Special tokens get -100\n", " elif word_idx != previous_word_idx:\n", " label_ids.append(label[word_idx]) # First token of word gets label\n", " else:\n", " label_ids.append(label[word_idx] if label_all_tokens else -100) # Subwords get label or -100\n", " previous_word_idx = word_idx\n", " labels.append(label_ids)\n", " tokenized_inputs[\"labels\"] = labels\n", " return tokenized_inputs\n", "\n", "# πŸ§ͺ Step 12: Test the tokenization and label alignment\n", "q = tokenize_and_align_labels(conll2025[\"train\"][0:1])\n", "print(\"Tokenized and aligned example:\", q)\n", "\n", "# πŸ“‹ Step 13: Print tokens and their corresponding labels\n", "for token, label in zip(tokenizer.convert_ids_to_tokens(q[\"input_ids\"][0]), q[\"labels\"][0]):\n", " print(f\"{token:_<40} {label}\")\n", "\n", "# πŸ”§ Step 14: Apply tokenization to the entire dataset\n", "tokenized_datasets = conll2025.map(tokenize_and_align_labels, batched=True)\n", "\n", "# πŸ€– Step 15: Initialize the model with the correct number of labels\n", "model = AutoModelForTokenClassification.from_pretrained(\"boltuix/bert-mini\", num_labels=num_tags)\n", "\n", "# βš™οΈ Step 16: Set up training arguments\n", "args = TrainingArguments(\n", " \"boltuix/bert-ner\",\n", " eval_strategy=\"epoch\", # Changed evaluation_strategy to eval_strategy\n", " learning_rate=2e-5,\n", " per_device_train_batch_size=16,\n", " per_device_eval_batch_size=16,\n", " num_train_epochs=1,\n", " weight_decay=0.01,\n", " report_to=\"none\"\n", ")\n", "# πŸ“Š Step 17: Initialize data collator for dynamic padding\n", "data_collator = DataCollatorForTokenClassification(tokenizer)\n", "\n", "# πŸ“ˆ Step 18: Load evaluation metric\n", "metric = evaluate.load(\"seqeval\")\n", "\n", "# 🏷️ Step 19: Set label list and test metric computation\n", "label_list = unique_tags\n", "print(\"Label list:\", label_list)\n", "example = conll2025[\"train\"][0]\n", "labels = [label_list[i] for i in example[\"ner_tags\"]]\n", "print(\"Metric test:\", metric.compute(predictions=[labels], references=[labels]))\n", "\n", "# πŸ“‰ Step 20: Define function to compute evaluation metrics\n", "def compute_metrics(eval_preds):\n", " \"\"\"\n", " Compute precision, recall, F1, and accuracy for NER.\n", "\n", " Args:\n", " eval_preds (tuple): Predicted logits and true labels.\n", "\n", " Returns:\n", " dict: Evaluation metrics.\n", " \"\"\"\n", " pred_logits, labels = eval_preds\n", " pred_logits = np.argmax(pred_logits, axis=2)\n", " predictions = [\n", " [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n", " for prediction, label in zip(pred_logits, labels)\n", " ]\n", " true_labels = [\n", " [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n", " for prediction, label in zip(pred_logits, labels)\n", " ]\n", " results = metric.compute(predictions=predictions, references=true_labels)\n", " return {\n", " \"precision\": results[\"overall_precision\"],\n", " \"recall\": results[\"overall_recall\"],\n", " \"f1\": results[\"overall_f1\"],\n", " \"accuracy\": results[\"overall_accuracy\"],\n", " }\n", "\n", "# πŸš€ Step 21: Initialize and train the trainer\n", "trainer = Trainer(\n", " model,\n", " args,\n", " train_dataset=tokenized_datasets[\"train\"],\n", " eval_dataset=tokenized_datasets[\"validation\"],\n", " data_collator=data_collator,\n", " tokenizer=tokenizer,\n", " compute_metrics=compute_metrics\n", ")\n", "trainer.train()\n", "\n", "# πŸ’Ύ Step 22: Save the fine-tuned model\n", "model.save_pretrained(\"boltuix/bert-ner\")\n", "tokenizer.save_pretrained(\"tokenizer\")\n", "\n", "# πŸ”— Step 23: Update model configuration with label mappings\n", "id2label = {str(i): label for i, label in enumerate(label_list)}\n", "label2id = {label: str(i) for i, label in enumerate(label_list)}\n", "config = json.load(open(\"boltuix/bert-ner/config.json\"))\n", "config[\"id2label\"] = id2label\n", "config[\"label2id\"] = label2id\n", "json.dump(config, open(\"boltuix/bert-ner/config.json\", \"w\"))\n", "\n", "# πŸ”„ Step 24: Load the fine-tuned model\n", "model_fine_tuned = AutoModelForTokenClassification.from_pretrained(\"boltuix/bert-ner\")\n", "\n", "# πŸ› οΈ Step 25: Create a pipeline for NER inference\n", "nlp = pipeline(\"token-classification\", model=model_fine_tuned, tokenizer=tokenizer)\n", "\n", "# πŸ“ Step 26: Perform NER on an example sentence\n", "example = \"On July 4th, 2023, President Joe Biden visited the United Nations headquarters in New York to deliver a speech about international law and donated $5 million to relief efforts.\"\n", "ner_results = nlp(example)\n", "print(\"NER results for first example:\", ner_results)\n", "\n", "# πŸ“ Step 27: Perform NER on a property address and format output\n", "example = \"This page contains information about the property located at 1275 Kinnear Rd, Columbus, OH, 43212.\"\n", "ner_results = nlp(example)\n", "\n", "# 🧹 Step 28: Process NER results into structured entities\n", "entities = defaultdict(list)\n", "current_entity = \"\"\n", "current_type = \"\"\n", "\n", "for item in ner_results:\n", " entity = item[\"entity\"]\n", " word = item[\"word\"]\n", " if word.startswith(\"##\"):\n", " current_entity += word[2:] # Handle subword tokens\n", " elif entity.startswith(\"B-\"):\n", " if current_entity and current_type:\n", " entities[current_type].append(current_entity.strip())\n", " current_type = entity[2:].lower()\n", " current_entity = word\n", " elif entity.startswith(\"I-\") and entity[2:].lower() == current_type:\n", " current_entity += \" \" + word # Continue same entity\n", " else:\n", " if current_entity and current_type:\n", " entities[current_type].append(current_entity.strip())\n", " current_entity = \"\"\n", " current_type = \"\"\n", "\n", "# Append final entity if exists\n", "if current_entity and current_type:\n", " entities[current_type].append(current_entity.strip())\n", "\n", "# πŸ“€ Step 29: Output the final JSON\n", "final_json = dict(entities)\n", "print(\"Structured NER output:\")\n", "print(json.dumps(final_json, indent=2))\n" ], "metadata": { "id": "6Dq_Ma5fTDHa", "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "db21622f7ac949938b770cd7f1e92411", "60f0bdff5c544afc97d6fad2ebb2643c", "51ec9848a4a342ec96df51989006a482", "103d08ac37f54b0abc977412e24bf1b5", "9e9e386cadeb4953a02cf74d5f190d0f", "e5478aac86aa495089f5f5dd6f277e91", "b7e098365b624b06b9137c9fc23b926e", "a082429851704c1990d1c68f7e035fdb", "3f1f58bbc5a7487a9933baf699ff9f6f", "d5e2e3db6801468bb45a743aafdb6578", "3683cbb8f99c400c8bb68938965e20a9", "9cbecd26be0d48c8a96a8279a96d4758", "d1bd3ccdc5104b1eb289d6366c7abbb8", "60b5c6ce4dea488498bb186a099b8c05", "7b1a258516414ba1983ef649be978c13", "96fd096ca4c845f2ad1088e81e0888e0", 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"a01685a80b2b44b6847b3879872883e6", "92c0f84601a345ae86e6f9e6584d7f95", "a9655e64155a4c59a666297bc84ace98" ] }, "outputId": "9348dece-5c64-41fc-9baa-4db8d167e248" }, "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset structure: Dataset({\n", " features: ['split', 'tokens', 'ner_tags'],\n", " num_rows: 143709\n", "})\n", "Dataset features: {'split': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ner_tags': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}\n", "First example: {'split': 'train', 'tokens': ['Big', 'Managers', 'on', 'Campus'], 'ner_tags': ['O', 'O', 'O', 'O']}\n", "Number of unique tags: 37\n", "Unique tags: ['B-CARDINAL', 'B-DATE', 'B-EVENT', 'B-FAC', 'B-GPE', 'B-LANGUAGE', 'B-LAW', 'B-LOC', 'B-MONEY', 'B-NORP', 'B-ORDINAL', 'B-ORG', 'B-PERCENT', 'B-PERSON', 'B-PRODUCT', 'B-QUANTITY', 'B-TIME', 'B-WORK_OF_ART', 'I-CARDINAL', 'I-DATE', 'I-EVENT', 'I-FAC', 'I-GPE', 'I-LANGUAGE', 'I-LAW', 'I-LOC', 'I-MONEY', 'I-NORP', 'I-ORDINAL', 'I-ORG', 'I-PERCENT', 'I-PERSON', 'I-PRODUCT', 'I-QUANTITY', 'I-TIME', 'I-WORK_OF_ART', 'O']\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/143709 [00:00:172: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n", " trainer = Trainer(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [7239/7239 02:27, Epoch 1/1]\n", "
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EpochTraining LossValidation LossPrecisionRecallF1Accuracy
10.3149000.2983310.4597550.4696320.4646410.916708

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.11/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "Device set to use cuda:0\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "NER results for first example: [{'entity': 'B-DATE', 'score': np.float32(0.4680284), 'index': 2, 'word': 'july', 'start': 3, 'end': 7}, {'entity': 'B-DATE', 'score': np.float32(0.40818664), 'index': 3, 'word': '4th', 'start': 8, 'end': 11}, {'entity': 'B-DATE', 'score': np.float32(0.27342722), 'index': 5, 'word': '202', 'start': 13, 'end': 16}, {'entity': 'I-DATE', 'score': np.float32(0.24528159), 'index': 6, 'word': '##3', 'start': 16, 'end': 17}, {'entity': 'B-PERSON', 'score': np.float32(0.5311531), 'index': 9, 'word': 'joe', 'start': 29, 'end': 32}, {'entity': 'I-PERSON', 'score': np.float32(0.475913), 'index': 10, 'word': 'bid', 'start': 33, 'end': 36}, {'entity': 'I-PERSON', 'score': np.float32(0.40657172), 'index': 11, 'word': '##en', 'start': 36, 'end': 38}, {'entity': 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