...understand language
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↺ Start over · Full treeYou are here: AI Solver › Understand unstructured data › ...understand language
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0 · Start
1 · Predict & analyze
tables, numbers, events
Predict values
Predict categories Forecast time series Cluster / segment Find outliers Make recommendations Find key drivers 2 · Understand unstructured data
text, documents, images, audio
3 · Generate & converse
4 · Decide & act
5 · Build it right
applies to every path
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YouTube ... Quora ...Google search ...Google News ...Bing News
- AI Solver ... Natural Language Processing (NLP) ... Sentiment Analysis ... Embedding ... Fine-tuning ... RAG
Use this page when your data is text: emails, tickets, reviews, contracts, chat logs, forms. Modern language models already "understand" text, so you rarely train from scratch; you choose how to use a pretrained model.
Start here
- What do you need from the text?
- Sort it into classes (sentiment, intent, topic, urgency, toxicity) → Sentiment Analysis
- Few or changing labels → prompt an LLM with label definitions and examples; ask for structured (JSON) output
- High volume, low latency or low cost → fine-tune a small encoder model (BERT-family such as DeBERTa or ModernBERT), or train a classifier on embeddings; SetFit works with a handful of examples per class
- Simple, transparent baseline → TF-IDF features + logistic regression or Naive Bayes
- Pull out facts (names, dates, amounts, product codes)
- From clean text → named-entity recognition (spaCy; GLiNER for entity types you name at run time) or an LLM filling a fixed schema
- From PDFs, scans and forms → document AI: OCR plus layout analysis (Azure AI Document Intelligence, Google Document AI, Amazon Textract) or parsers built on vision-language models (Mistral OCR, Docling, LlamaParse); validate extracted fields
- Summarize, rewrite or draft → an LLM; check the summary against the source for invented facts → ...generate content
- Translate → neural machine translation (DeepL, Google, Microsoft) or an LLM; low-resource languages → Meta's NLLB and SeamlessM4T models; spoken language → ...work with speech and audio
- Find similar items, search, deduplicate → embeddings + vector search (compare models on the MTEB leaderboard) → ...answer questions from my data
- Answer questions from documents → ...answer questions from my data
- Discover themes in thousands of comments → ...cluster (BERTopic)
- Hold a conversation and take actions → ...build an agent
- Sort it into classes (sentiment, intent, topic, urgency, toxicity) → Sentiment Analysis
- Must the text stay inside your network? → self-hosted open-weight model: ...choose a model and ...deploy AI
Quick picks
| Task | Start with | Scale up with |
|---|---|---|
| Classify (low volume) | Prompted LLM + JSON output | LLM-labeled data → fine-tuned small model |
| Classify (high volume) | Embeddings + logistic regression | Fine-tuned DeBERTa / ModernBERT |
| Extract fields from text | LLM with a schema | NER model (spaCy, GLiNER) |
| Extract from scans and forms | Cloud document AI | VLM-based parsing + validation rules |
| Summarize | LLM | Retrieval + citations for long sources |
| Translate | Neural machine translation | LLM with a glossary and style guide |
Real-world examples
- Multilingual customer service — Klarna's AI assistant handled about 2.3 million conversations in its first month, across more than 35 languages; Klarna later added human agents back where quality suffered. See ...build an agent.
- Meeting notes — Morgan Stanley's AI @ Morgan Stanley Debrief creates notes and action items from client meetings (with client consent), drafts a follow-up email for the advisor to edit, and saves a note to the CRM. AI @ Morgan Stanley Debrief launch | Morgan Stanley
Watch out
- LLMs can invent facts in summaries and extractions; verify against the source, especially numbers and names.
- Prompt injection — text you process can contain instructions; never let processed text trigger actions without checks.
- Personal data — redact or minimize PII before sending text to external services.
Measure success
Accuracy, precision, recall and F1 for classification; field-level accuracy for extraction; faithfulness and coverage for summaries; human preference for writing quality. More: ...evaluate and trust AI
Learn more
- LLM course | Hugging Face
- MTEB embedding leaderboard | Hugging Face
- spaCy: industrial-strength NLP
- Natural Language Processing (NLP) ... Sentiment Analysis ... Embedding ... Fine-tuning