NLP & Language Models Virtual Lab

Explore Natural Language Processing and modern language models interactively

⚡

🌟 Enhanced Training

Full hyperparameter control & visualization

🔤

Word Embeddings

Word2Vec, GloVe, and semantic spaces

👁️

Attention Mechanism

Self-attention and multi-head attention

🤖

Transformer Architecture

Encoder-decoder and BERT/GPT models

✂️

Tokenization

BPE, WordPiece, and subword tokenization

🔄

Sequence-to-Sequence

Translation and text generation

😊

Sentiment Analysis

Text classification and emotion detection

🗣️ Enhanced NLP Training - Language Model Training Simulator

Train NLP models step-by-step with full control over architecture and hyperparameters. Watch attention patterns evolve and performance improve.

⚙️ Model Configuration

Core model architecture

Lower for fine-tuning

Samples per gradient update

Training iterations

Token representation size

Multi-head attention

Regularization strength

🎮 Training Controls

Epoch 0 / 500.0%

Train Loss

0.000

Val Loss

0.000

Train Acc

0.0%

Val Acc

0.0%

BLEU Score

0.0

📊 Training Metrics

Loss Curves

Accuracy & BLEU Score

Attention Weights (Epoch 0)

📋 Training History

EpochTrain LossVal LossTrain AccVal AccBLEU

📐 Transformer Equations

Scaled Dot-Product Attention:

Attention(Q,K,V)=softmax(QKTdk)V\text{Attention}(Q,K,V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V

Multi-Head Attention:

MultiHead(Q,K,V)=Concat(head1,...,headh)WO\text{MultiHead}(Q,K,V) = \text{Concat}(\text{head}_1,...,\text{head}_h)W^O

Positional Encoding:

PE(pos,2i)=sin⁡(pos100002i/d)PE_{(pos,2i)} = \sin\left(\frac{pos}{10000^{2i/d}}\right)

Cross-Entropy Loss:

L=−1N∑i=1N∑c=1Vyi,clog⁡(y^i,c)\mathcal{L} = -\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{V} y_{i,c} \log(\hat{y}_{i,c})

💡 Understanding NLP Models

  • • Transformer: Self-attention mechanism processes all tokens in parallel
  • • LSTM Seq2Seq: Encoder-decoder with sequential processing and attention
  • • BERT Fine-tune: Pre-trained bidirectional model adapted for specific tasks
  • • Attention Heads: Multiple heads capture different linguistic relationships
  • • Embedding Dim: Higher dimensions capture richer token representations
  • • BLEU Score: Measures translation/generation quality (0-100)