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NLP Projects for Final Year: 10 Ideas With Datasets and Metrics

Satvik ParamkushamUpdated October 5, 202613 min read

Quick answer

Strong NLP projects for final year include fake news detection with evidence, named entity recognition for Indian languages, English to Indian language translation, Hindi headline generation, Hinglish offensive content detection and text-to-SQL. Use a published dataset, compare a baseline with a transformer model, and report the standard metric for the task: F1, BLEU, chrF, ROUGE or exact match.

Natural language processing is one of the best areas for a final-year project because the problems are easy to explain and the results are easy to show. Type a sentence, get an answer, and the examiner understands what your project does in ten seconds.

The catch is that NLP is also where examiners have seen the most repeated projects. A basic spam filter or a generic chatbot will not stand out in 2026. What stands out is a project on a harder task, a project in an Indian language, or a project with careful evaluation using the metric researchers actually use for that task.

Below are 10 NLP project ideas built on datasets that exist and are free to download, with the right metric for each. If you want a gentler first NLP project before tackling these, start with our sentiment analysis build. For non-NLP machine learning ideas, see machine learning projects for final year.

What makes a good NLP project for final year?

A good NLP final-year project has a clearly defined task, a published dataset, a simple baseline, a modern model and an honest evaluation with the standard metric for that task. If your project has all five, it is already better than most submissions.

What examiners and interviewers look for in an NLP project specifically:

  • A real language problem: misinformation, legal reading, access to information in Indian languages, or searching a database without SQL.
  • A baseline: TF-IDF with logistic regression, or a rule-based system, so your transformer's improvement means something.
  • Error analysis: 20 wrong outputs grouped by cause, such as negation, sarcasm, rare names or code-mixing.
  • Understanding of the model: be ready to explain transformers, tokens and why Indian scripts often split into more tokens than English.

Which datasets and metrics should an NLP project use?

Use a dataset that other researchers have published results on, and the metric that comes with that task, so your numbers can be compared with something. Here is a quick map for the ideas on this page:

TaskExample datasetMain metric
Fake news or claim classificationLIAR, FEVERMacro F1, accuracy
Named entity recognitionNaamapadam, CoNLL-2003Entity-level F1
TranslationSamanantar (train), FLORES-200 or IN22 (test)BLEU, chrF
Summarisation and headline generationCNN/DailyMail, IndicHeadlineGenerationROUGE-1, ROUGE-2, ROUGE-L
Extractive question answeringIndicQA, SQuADExact match, token F1
Text-to-SQLSpiderExecution accuracy
TransliterationAksharantarTop-1 word accuracy, character error rate

Check each dataset's licence before you start. Some, such as the IndicNLG datasets, are licensed for non-commercial research only, which is fine for a college project but matters if you later turn it into a product.

Should an NLP project use classical models, transformers or an LLM?

Use all three where you can: a classical baseline, a fine-tuned transformer as your main model, and an LLM as a comparison. That three-way comparison is one of the easiest ways to turn an average NLP project into a strong one.

  • Classical (TF-IDF, logistic regression, CRF): fast, explainable and surprisingly hard to beat on small classification datasets.
  • Fine-tuned transformers (BERT, IndicBERT, MuRIL, XLM-RoBERTa, mT5, IndicBART): usually the best accuracy for a fixed task. Most fine-tune in Google Colab with a free GPU session if you keep the model size small.
  • LLMs with prompting: no training needed, good for a zero-shot comparison, but harder to evaluate and prone to hallucination on generation tasks.

If you are unsure how fine-tuning differs from prompting, settle that before your viva; it is one of the first questions asked.

10 NLP project ideas for final year students

Every dataset below is publicly available at the time of writing; some require accepting terms or registering. Time estimates assume a team of two or three working part-time over a semester.

  1. 1. Fake news detection with evidence retrieval

    Level:
    Intermediate to advanced
    Tools:
    Python, scikit-learn, transformers
    Metric:
    Macro F1

    Classify political claims by truthfulness, then go further by retrieving evidence that supports or refutes each claim. The LIAR dataset has about 12,800 short statements from PolitiFact labelled on a six-point scale from "pants-fire" to "true". Start with TF-IDF and logistic regression, then fine-tune a BERT-style model and report macro F1, since six-way labels are hard and accuracy alone hides weak classes. Upgrade: use the FEVER dataset, where claims must be verified against Wikipedia sentences, and build a retrieve-then-classify pipeline. Showing the evidence sentence next to each verdict makes the demo far more convincing than a bare label.

  2. 2. Named entity recognition for Indian languages

    Level:
    Intermediate
    Tools:
    Python, transformers, seqeval
    Metric:
    Entity-level F1

    Detect names of people, places and organisations in Hindi, Tamil, Bengali or another Indian language. AI4Bharat's Naamapadam dataset covers 11 Indian languages with labels that follow CoNLL-2003 style guidelines, and has manually checked test sets. Fine-tune a multilingual model such as IndicBERT or MuRIL with a token classification head, and report entity-level precision, recall and F1 using the seqeval library. Upgrade: train on one language and test on another (zero-shot cross-lingual transfer), or compare performance on news text against informal social media text you annotate yourself.

  3. 3. English to Indian language translation for a real domain

    Level:
    Advanced
    Tools:
    Python, transformers, sacreBLEU
    Metric:
    BLEU, chrF, human rating

    Translate public information, such as scholarship notices, health advisories or college circulars, from English into an Indian language, and measure quality honestly. Use AI4Bharat's open IndicTrans2 models, which cover all 22 scheduled Indian languages, and compare them with a general-purpose LLM. Evaluate on a public benchmark such as FLORES-200 or IN22 using BLEU and chrF (sacreBLEU computes both), then on 50 sentences from your own domain checked by native speakers. Upgrade: fine-tune a smaller distilled model on domain sentences from the Samanantar parallel corpus and show whether domain scores improve.

  4. 4. Headline generation in Hindi and other Indian languages

    Level:
    Intermediate to advanced
    Tools:
    Python, transformers, evaluate
    Metric:
    ROUGE, human rating

    Generate a short headline from a news article in an Indian language. The IndicHeadlineGeneration dataset, part of AI4Bharat's IndicNLG suite, has article and headline pairs in 11 Indian languages. Fine-tune a sequence-to-sequence model such as IndicBART or mT5-small, and report ROUGE-1, ROUGE-2 and ROUGE-L. Note that ROUGE was designed for English, so explain how you tokenised the Indian-language text before scoring. Upgrade: add a human evaluation where three readers rate fluency and faithfulness for 30 headlines, and show where ROUGE disagrees with people.

  5. 5. Long document summariser with extractive vs abstractive comparison

    Level:
    Intermediate
    Tools:
    Python, transformers, evaluate
    Metric:
    ROUGE-1, ROUGE-2, ROUGE-L

    Summarise long English articles and compare two approaches: extractive (pick the most important sentences, for example with TextRank) and abstractive (generate new sentences with a model such as BART or T5). The CNN/DailyMail dataset on Hugging Face has news articles with highlight-style summaries and is the standard starting point. Report ROUGE scores for both, plus summary length and a count of factual errors in 20 summaries. Upgrade: apply the best model to a domain you care about, such as Supreme Court judgments or research papers, and handle documents longer than the model's context limit by chunking.

  6. 6. Offensive and hate content detection in Hindi and Hinglish

    Level:
    Intermediate
    Tools:
    Python, scikit-learn, transformers
    Metric:
    Macro F1

    Detect offensive or hateful posts in Hindi and English social media text, including Roman-script Hindi mixed with English. The HASOC shared task, run with the FIRE conference, has released hate and offensive content datasets for English and Indo-Aryan languages including Hindi; access may need registration. Classes are imbalanced, so report macro F1 and a confusion matrix. Compare a TF-IDF character n-gram model with a multilingual transformer, since code-mixed spelling varies wildly. Upgrade: build a small test set of Hinglish posts you annotate with two classmates and report how often you agreed with each other before trusting the labels.

  7. 7. Roman to native script transliteration

    Level:
    Intermediate
    Tools:
    Python, PyTorch
    Metric:
    Top-1 accuracy, CER

    Convert words typed in Roman letters, such as "dhanyavaad", into native scripts like Devanagari or Tamil, the problem every Indian-language keyboard has to solve. AI4Bharat's Aksharantar dataset has about 26 million word pairs between English script and Indian scripts. Train a character-level sequence-to-sequence model, starting with a small LSTM encoder-decoder and then a transformer, and report top-1 word accuracy and character error rate. Upgrade: add beam search to return the top five candidates, as a keyboard would, and measure whether the right word appears in the list.

  8. 8. Question answering in Indian languages

    Level:
    Advanced
    Tools:
    Python, transformers
    Metric:
    Exact match, F1

    Build a system that finds the answer span inside a paragraph for a question asked in an Indian language. The IndicQA dataset from AI4Bharat follows the SQuAD format across 10 Indian languages. Fine-tune a multilingual model such as XLM-RoBERTa or MuRIL, possibly first on English SQuAD, and report exact match and token-level F1. Discuss why exact match is harsh for languages with rich word endings. Upgrade: measure how much English-only training helps or hurts each language, and present it as a per-language table.

  9. 9. Natural language to SQL for a college database

    Level:
    Intermediate to advanced
    Tools:
    Python, SQLite, an LLM API
    Metric:
    Execution accuracy

    Let staff ask "how many students in third year have attendance below 75%?" and get the answer from a database without writing SQL. The Spider dataset has over 10,000 questions with SQL queries across 200 databases and remains a standard learning benchmark, though its authors now point researchers to the harder Spider 2.0. Prompt an LLM with the database schema, run the generated SQL on a copy of the database and report execution accuracy. Upgrade: build a small mock college database, write 50 test questions, and add a safety layer that blocks any query that is not a read-only SELECT.

  10. 10. Contract clause extraction for legal review

    Level:
    Advanced
    Tools:
    Python, transformers
    Metric:
    Precision, recall, F1

    Highlight important clauses in a contract, such as termination, governing law or non-compete, so a reviewer can find them fast. The CUAD dataset from The Atticus Project has 510 commercial contracts with over 13,000 expert labels across 41 clause types. Frame it as extractive question answering ("Highlight the parts related to governing law") and fine-tune a transformer, reporting precision and recall per clause type. Upgrade: compare the fine-tuned model with an LLM prompted to extract the same clauses, and count how often each invents a clause that is not in the document.

How do you explain BLEU, ROUGE and F1 in a viva?

Explain each metric as a way of comparing your model's output with a human reference: BLEU checks how much of your translation's wording appears in the reference, ROUGE checks how much of the reference's wording appears in your summary, and F1 balances precision and recall for labels.

  • BLEU: counts matching word sequences (n-grams) between a machine translation and one or more reference translations, with a penalty for outputs that are too short. Higher is better, but a correct translation with different wording can still score low.
  • chrF: like BLEU but on characters, which is fairer for Indian languages where one word can take many endings.
  • ROUGE: measures overlap between a generated summary and a reference summary. ROUGE-1 uses single words, ROUGE-2 uses word pairs and ROUGE-L uses the longest common subsequence.
  • F1: the harmonic mean of precision (how many predicted items were right) and recall (how many true items were found). Macro F1 averages across classes, so small classes count as much as big ones.
  • Exact match: the percentage of answers that match the reference exactly, character for character after normalisation.

The honest line every examiner likes: "Automatic metrics are a proxy, so I also checked a sample by hand."

What should an NLP final year project report include?

An NLP final year project report should include the task definition, dataset details, preprocessing and tokenisation, the baseline and main models, results with the standard metric, error analysis and limitations. Most departments accept this order:

  1. Introduction and problem statement
  2. Related work: three to five papers on the same task or dataset
  3. Dataset: source, licence, size, language, label distribution and splits
  4. Methodology: preprocessing, baseline, main model and training settings
  5. Results: one table comparing all models on the test set
  6. Error analysis with real examples
  7. Limitations, ethics and future work

Viva questions to prepare for: Why this tokeniser? How did you handle class imbalance? What does your model do with a word it has never seen? Why is your BLEU or ROUGE score lower than the paper you cited? For model-level questions, our LLM interview questions and the explainer on the attention mechanism cover the theory examiners probe.

What mistakes do students make in NLP projects?

The biggest mistake is evaluating on data the model has already seen, such as near-duplicate news articles across train and test sets. Other common ones:

  1. Reporting accuracy on imbalanced classes: if 90% of posts are not offensive, 90% accuracy means nothing. Use macro F1.
  2. Comparing BLEU or ROUGE scores computed differently: tokenisation and settings change the number. Use sacreBLEU and the same ROUGE setup for every model.
  3. Ignoring script and spelling variation: Hinglish words have many spellings. Normalise text or use character-level features.
  4. No human check on generated text: a fluent summary can still state facts that are not in the article.
  5. Using a dataset without reading its licence or labelling guide: you cannot explain your labels if you never read how they were made.

Frequently asked questions

Which NLP project is best for final year?

The best NLP final-year projects tackle a task beyond basic classification, such as fact verification, Indian language NER, translation or text-to-SQL. Choose one with a published dataset and a standard metric, compare a baseline with a transformer, and include error analysis. Projects in Indian languages stand out because fewer students attempt them.

What are good NLP project ideas for beginners?

Good beginner NLP projects are sentiment analysis, spam detection, news topic classification and keyword extraction. They use small labelled datasets, run on a laptop with scikit-learn and teach tokenisation, TF-IDF and evaluation. Once comfortable, move to a fine-tuned transformer on the same task before attempting the final-year ideas.

Where can I find datasets for NLP projects in Indian languages?

AI4Bharat at IIT Madras publishes open datasets and models for Indian languages, including Naamapadam for NER, Aksharantar for transliteration, IndicQA for question answering and the IndicNLG suite for generation. Most are on Hugging Face. Read each dataset's licence, since some allow only non-commercial research use.

What is the difference between BLEU and ROUGE?

BLEU is mainly used for machine translation and is precision-oriented: it checks how much of the generated text appears in the reference. ROUGE is mainly used for summarisation and is recall-oriented: it checks how much of the reference appears in the generated summary. Both measure word overlap, not meaning.

Can I do an NLP final year project without a GPU?

Yes. Classical baselines run on any laptop, and small transformer models such as DistilBERT or mT5-small can be fine-tuned in Google Colab using its limited free GPU sessions. For LLM comparisons, hosted APIs avoid local hardware entirely. Keep datasets and models modest and save checkpoints often.

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References

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