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Solve NLP tasks (classification, NER, similarity)

Tokenization, transformers via Hugging Face, fine-tune small encoders for classification/NER.

~20 focused hoursintermediate
Explore 3 tools for this project
Market relevance

Which roles ask for this — and how often

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What employers mean

You should be able to…

  1. Tokenize and preprocess text correctly for a transformer model (truncation, padding, special tokens)
  2. Fine-tune a small pretrained encoder (BERT/DistilBERT) for text classification
  3. Build a Named Entity Recognition (NER) model for a domain-specific entity set
  4. Compute text similarity/embeddings for search or deduplication
  5. Handle multilingual or code-mixed text (e.g. Hindi, Hinglish) preprocessing challenges
  6. Evaluate classification/NER with the right metrics (F1, precision/recall per class, not just accuracy)
  7. Know when a classic ML baseline (TF-IDF + logistic regression) beats a transformer for a small dataset

Needs first: Train and evaluate classical ML models

Learn — free, link-checked

The few resources that matter

Course · beginner · 60 min · huggingface.co

Introduction

The standard free path into transformers -- pipelines, tokenizers and the Transformer architecture from the team that builds the library. — Hugging Face
Read · intermediate · 40 min · huggingface.co

Text classification

Copy-correct recipe for fine-tuning a small encoder (e.g. DistilBERT) for classification -- directly reusable for a support-ticket or sentiment model. — Hugging Face
Read · intermediate · 40 min · huggingface.co

Token classification

Official NER fine-tuning recipe with the BIO-tagging and evaluation details classical-NLP interview questions probe for. — Hugging Face
Tools for practice

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3 tools to explore

Hugging Face Transformers

Build · Data

Load pretrained models and adapt them to a text, image or multimodal task.

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Practice

Hindi support-message intent classifier and entity extractor

Use the hi-IN split of the MASSIVE dataset on Hugging Face — 16.5k Hindi utterances labelled with intents and slot spans — as a stand-in for an incoming support queue. Build a TF-IDF + logistic-regression baseline first, then fine-tune a multilingual encoder (mBERT, IndicBERT or MuRIL) on the same label set and compare per-class F1. Add the slot/entity head so the system extracts the useful fields, not just a category. Finally, rewrite a sample of the test set into romanised Hinglish and measure how much accuracy you lose — that gap is the real finding.

Start from

MASSIVE dataset, hi-IN split, on Hugging Face — 16.5k Hindi utterances labelled with 60 intents and slot spans

Milestones
  1. Load the hi-IN split, collapse it to a working label set, build the TF-IDF baseline · ~3.5h
  2. Fine-tune a multilingual encoder on the same intent task · ~5.5h
  3. Train the slot/NER head and inspect extractions on real utterances · ~5.5h
  4. Probe with romanised Hinglish rewrites and document what breaks · ~3.5h
Done when
  • Labeled dataset (100+ examples) with a documented labeling scheme and train/test split
  • Both TF-IDF baseline and fine-tuned transformer trained, with F1 per class compared in a table
  • NER or entity extraction component demonstrated on at least 10 example tickets with correct/incorrect cases shown
  • README honestly states which approach won and why, including any code-mixing failure cases found
Prove it

Evidence a recruiter can check

  • A per-class F1 table for TF-IDF vs the fine-tuned encoder, calling out the classes where the cheap baseline still wins
  • Ten annotated slot-extraction examples with correct and wrong spans shown side by side
  • The script-gap measurement: the same messages in Devanagari and in romanised Hinglish, with the accuracy drop between them
  • A tokenizer inspection — a few Hindi sentences printed as token ids — explaining why subword splits hurt specific classes
Signal it

Built a Hindi support-message triage model on 16.5k labelled utterances — a fine-tuned multilingual encoder beat a TF-IDF baseline on per-class F1, with slot extraction and a measured accuracy drop on romanised Hinglish input.

Interview

Questions you'll get asked

  1. How would you build a classifier to route support tickets into 5 categories, and what would you try first?
  2. Walk me through fine-tuning BERT for a classification task -- what changes vs training from scratch?
  3. How do you evaluate a NER model, and why isn't accuracy the right metric?
  4. What challenges come up with Hindi or Hinglish (code-mixed) text that don't come up in English?
  5. When would a TF-IDF + logistic regression baseline be a better choice than fine-tuning a transformer?
  6. How do you handle class imbalance in a multi-class text classification problem?
  7. What's the difference between a tokenizer's vocabulary and the model's embedding layer?
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