Post by Dhananjay N.

SkillCourse Satish Dhawale #BERT is a transformer-based model created by #Google in 2018 to excel at natural language processing (#NLP) tasks. Unlike older models that read text left-to-right only, BERT reads sentences #bidirectionally, capturing deeper meaning—like knowing "bank" means a river edge or money place based on surrounding words. It uses an encoder-only #architecture stacked in layers (typically 12 or 24), turning text into numerical vectors that represent context. 💢 Why Bidirectional Matters Imagine reading a sentence backward and forward at once: "The bank by the river was flooded." BERT understands "bank" as a riverbank here, not finance, because it sees all words simultaneously. This beats one-way models like GPT (decoder-focused) by enabling true comprehension for tasks like translation or summarization. 👍 Step 1: Preparing Input Text gets #tokenized into sub-words (e.g., "playing" becomes "play" + "##ing"). Add special tokens: [CLS] at start for whole-sentence summary, [SEP] to separate sentences and [MASK] for training. Each #token becomes a #vector with three embeddings added: token itself, position (word order) and segment (which sentence). 👍 Step 2: Pre-Training BERT BERT learns on massive text like Wikipedia via two tasks—no labels needed. Masked Language Model (#MLM): Randomly mask 15% of words; predict them from context. E.g., "The [MASK] ran fast" → "dog". Next Sentence Prediction (#NSP): 50% time, feed sentence A + real next B; else random. Predict if B follows A. This builds contextual smarts over millions of steps. 👍 Step 3: Fine-Tuning for Use Pre-trained BERT adds a simple task-specific layer (e.g., classifier) and trains briefly on labeled data. For sentiment: Input review → [CLS] vector → "positive/negative" output. Takes hours, not weeks. 💢 Common Uses Search: Google ranks results better (e.g., "find 2019 Brazil traveler needs"). QA: Answer questions from docs. Classification: Spam detection, sentiment. More: Translation, NER (name entity recognition). Start experimenting via Hugging Face libraries in Python—load pre-trained BERT and fine-tune easily.