diff --git "a/EntityBERT.ipynb" "b/EntityBERT.ipynb" new file mode 100644--- /dev/null +++ "b/EntityBERT.ipynb" @@ -0,0 +1,5584 @@ +{ + "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", + 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+ " 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%| | 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EpochTraining LossValidation LossPrecisionRecallF1Accuracy
10.3149000.2983310.4597550.4696320.4646410.916708

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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': 'B-ORG', 'score': np.float32(0.26126653), 'index': 13, 'word': 'the', 'start': 47, 'end': 50}, {'entity': 'B-ORG', 'score': np.float32(0.22172953), 'index': 14, 'word': 'united', 'start': 51, 'end': 57}, {'entity': 'I-ORG', 'score': np.float32(0.38466632), 'index': 15, 'word': 'nations', 'start': 58, 'end': 65}, {'entity': 'B-GPE', 'score': np.float32(0.59206396), 'index': 18, 'word': 'new', 'start': 82, 'end': 85}, {'entity': 'B-GPE', 'score': np.float32(0.5719239), 'index': 19, 'word': 'york', 'start': 86, 'end': 90}, {'entity': 'I-MONEY', 'score': np.float32(0.41128817), 'index': 29, 'word': '$', 'start': 147, 'end': 148}, {'entity': 'I-MONEY', 'score': np.float32(0.525152), 'index': 30, 'word': '5', 'start': 148, 'end': 149}, {'entity': 'I-MONEY', 'score': np.float32(0.57229793), 'index': 31, 'word': 'million', 'start': 150, 'end': 157}]\n", + "Structured NER output:\n", + "{\n", + " \"org\": [\n", + " \"kinnear rd\"\n", + " ],\n", + " \"gpe\": [\n", + " 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