181 lines
4.1 KiB
Markdown
181 lines
4.1 KiB
Markdown
---
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name: nlp-pipeline-builder
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description: |
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Natural language processing ML pipelines for text classification, NER, sentiment analysis, text generation, and embeddings. Activates for "nlp", "text classification", "sentiment analysis", "named entity recognition", "BERT", "transformers", "text preprocessing", "tokenization", "word embeddings". Builds NLP pipelines with transformers, integrated with SpecWeave increments.
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---
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# NLP Pipeline Builder
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## Overview
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Specialized ML pipelines for natural language processing. Handles text preprocessing, tokenization, transformer models (BERT, RoBERTa, GPT), fine-tuning, and deployment for production NLP systems.
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## NLP Tasks Supported
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### 1. Text Classification
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```python
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from specweave import NLPPipeline
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# Binary or multi-class text classification
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pipeline = NLPPipeline(
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task="classification",
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classes=["positive", "negative", "neutral"],
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increment="0042"
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)
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# Automatically configures:
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# - Text preprocessing (lowercase, clean)
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# - Tokenization (BERT tokenizer)
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# - Model (BERT, RoBERTa, DistilBERT)
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# - Fine-tuning on your data
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# - Inference pipeline
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pipeline.fit(train_texts, train_labels)
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```
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### 2. Named Entity Recognition (NER)
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```python
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# Extract entities from text
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pipeline = NLPPipeline(
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task="ner",
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entities=["PERSON", "ORG", "LOC", "DATE"],
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increment="0042"
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)
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# Returns: [(entity_text, entity_type, start_pos, end_pos), ...]
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```
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### 3. Sentiment Analysis
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```python
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# Sentiment classification (specialized)
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pipeline = NLPPipeline(
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task="sentiment",
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increment="0042"
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)
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# Fine-tuned for sentiment (positive/negative/neutral)
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```
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### 4. Text Generation
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```python
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# Generate text continuations
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pipeline = NLPPipeline(
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task="generation",
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model="gpt2",
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increment="0042"
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)
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# Fine-tune on your domain-specific text
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```
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## Best Practices for NLP
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### Text Preprocessing
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```python
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from specweave import TextPreprocessor
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preprocessor = TextPreprocessor(increment="0042")
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# Standard preprocessing
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preprocessor.add_steps([
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"lowercase",
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"remove_html",
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"remove_urls",
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"remove_emails",
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"remove_special_chars",
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"remove_extra_whitespace"
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])
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# Advanced preprocessing
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preprocessor.add_advanced([
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"spell_correction",
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"lemmatization",
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"stopword_removal"
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])
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```
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### Model Selection
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**Text Classification**:
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- Small datasets (<10K): DistilBERT (6x faster than BERT)
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- Medium datasets (10K-100K): BERT-base
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- Large datasets (>100K): RoBERTa-large
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**NER**:
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- General: BERT + CRF layer
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- Domain-specific: Fine-tune BERT on domain corpus
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**Sentiment**:
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- Product reviews: DistilBERT fine-tuned on Amazon reviews
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- Social media: RoBERTa fine-tuned on Twitter
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### Transfer Learning
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```python
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# Start from pre-trained language models
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pipeline = NLPPipeline(task="classification")
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# Option 1: Use pre-trained (no fine-tuning)
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pipeline.use_pretrained("distilbert-base-uncased")
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# Option 2: Fine-tune on your data
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pipeline.use_pretrained_and_finetune(
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model="bert-base-uncased",
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epochs=3,
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learning_rate=2e-5
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)
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```
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### Handling Long Text
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```python
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# For text longer than 512 tokens
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pipeline = NLPPipeline(
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task="classification",
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max_length=512,
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truncation_strategy="head_and_tail" # Keep start + end
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)
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# Or use Longformer for long documents
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pipeline.use_model("longformer") # Handles 4096 tokens
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```
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## Integration with SpecWeave
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```python
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# NLP increment structure
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.specweave/increments/0042-sentiment-classifier/
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├── spec.md
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├── data/
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│ ├── train.csv
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│ ├── val.csv
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│ └── test.csv
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├── models/
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│ ├── tokenizer/
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│ ├── model-epoch-1/
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│ ├── model-epoch-2/
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│ └── model-epoch-3/
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├── experiments/
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│ ├── distilbert-baseline/
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│ ├── bert-base-finetuned/
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│ └── roberta-large/
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└── deployment/
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├── model.onnx
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└── inference.py
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```
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## Commands
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```bash
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/ml:nlp-pipeline --task classification --model bert-base
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/ml:nlp-evaluate 0042 # Evaluate on test set
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/ml:nlp-deploy 0042 # Export for production
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```
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Quick setup for NLP projects with state-of-the-art transformer models.
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