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README.md

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+---
+language: en
+license: apache-2.0
+tags:
+  - consumer-finance
+  - complaints
+  - text-classification
+  - cfpb
+  - nlp
+size_categories:
+  - 100K<n<1M
+task_categories:
+  - text-classification
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: train.parquet
+      - split: test
+        path: test.parquet
+---
+
+# Consumer Finance Complaints — 11 Consolidated Labels
+
+A cleaned, deduplicated, and label-consolidated version of the [US CFPB Consumer Complaints dataset](https://www.consumerfinance.gov/data-research/consumer-complaints/) for **multi-class text classification**.
+
+## Context
+
+This dataset was prepared as part of **Project 12 — "Compare AI Algorithms: Machine Learning vs. LLM"** of the [OpenClassrooms AI Developer certification](https://openclassrooms.com/) by **William Derue**.
+
+The project scenario involves **ZenAssist**, a customer support platform serving 200+ companies that wants to automatically label incoming consumer complaints to route them to the correct support department.
+
+### Source
+
+The raw dataset was provided by OpenClassrooms:
+
+```
+https://s3.eu-west-1.amazonaws.com/course.oc-static.com/projects/2464_Développeur_IA/P12/dataset.csv
+```
+
+## Dataset Description
+
+Each row contains a consumer complaint text and its product category label.
+
+| Column  | Type         | Description                                                 |
+| ------- | ------------ | ----------------------------------------------------------- |
+| `text`  | `string`     | The consumer complaint narrative (variable length, English) |
+| `label` | `ClassLabel` | Product category — one of 11 consolidated labels            |
+
+### Splits
+
+| Split   |    Rows | Usage      |
+| ------- | ------: | ---------- |
+| `train` | 293,698 | Training   |
+| `test`  |  73,425 | Evaluation |
+
+Total: **367,123 rows** after cleaning.
+
+### Label Distribution (full dataset)
+
+| Label                   |   Count |     % |
+| ----------------------- | ------: | ----: |
+| Credit reporting        | 110,293 | 30.0% |
+| Debt collection         |  84,318 | 23.0% |
+| Mortgage                |  52,945 | 14.4% |
+| Credit card             |  41,498 | 11.3% |
+| Bank account            |  27,714 |  7.5% |
+| Student loan            |  21,781 |  5.9% |
+| Consumer Loan           |   9,443 |  2.6% |
+| Money transfer          |   6,953 |  1.9% |
+| Payday loan             |   6,155 |  1.7% |
+| Vehicle loan or lease   |   5,720 |  1.6% |
+| Other financial service |     303 |  0.1% |
+
+## Label Consolidation
+
+The original CFPB dataset contains **18 raw product tags** with significant semantic overlap. We consolidated them into **11 categories**:
+
+| Original Label                                                               | Consolidated Label          |
+| ---------------------------------------------------------------------------- | --------------------------- |
+| Credit reporting, credit repair services, or other personal consumer reports | **Credit reporting**        |
+| Credit reporting                                                             | **Credit reporting**        |
+| Credit card                                                                  | **Credit card**             |
+| Credit card or prepaid card                                                  | **Credit card**             |
+| Prepaid card                                                                 | **Credit card**             |
+| Bank account or service                                                      | **Bank account**            |
+| Checking or savings account                                                  | **Bank account**            |
+| Payday loan                                                                  | **Payday loan**             |
+| Payday loan, title loan, or personal loan                                    | **Payday loan**             |
+| Money transfer, virtual currency, or money service                           | **Money transfer**          |
+| Money transfers                                                              | **Money transfer**          |
+| Mortgage                                                                     | **Mortgage**                |
+| Debt collection                                                              | **Debt collection**         |
+| Student loan                                                                 | **Student loan**            |
+| Consumer Loan                                                                | **Consumer Loan**           |
+| Vehicle loan or lease                                                        | **Vehicle loan or lease**   |
+| Other financial service                                                      | **Other financial service** |
+| Virtual currency                                                             | **Other financial service** |
+
+### Rationale
+
+- **Semantic deduplication**: "Credit card" and "Credit card or prepaid card" describe the same product family.
+- **Virtual currency** (9 raw samples) was merged into **Other financial service** — too rare to learn as a standalone class.
+- This consolidation reduces label ambiguity and improves classifier performance without losing meaningful distinctions.
+
+## Preprocessing
+
+1. **Text column selection**: Used `Consumer Claim` (or `Customer Claim`) as the text field.
+2. **Cleaning**: Stripped whitespace, removed empty rows and NaN values.
+3. **Deduplication**: Dropped duplicate `(text, label)` pairs → 367,123 unique rows.
+4. **Label mapping**: Applied the consolidation map above.
+5. **Split**: 80/20 stratified train/test split (seed=3407).
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("WillisBack/dataset-financial-user-claim")
+print(ds)
+# DatasetDict({
+#     train: Dataset({features: ['text', 'label'], num_rows: 293698})
+#     test:  Dataset({features: ['text', 'label'], num_rows: 73425})
+# })
+
+# Access a sample
+print(ds["train"][0])
+# {'text': 'I was charged an overdraft fee...', 'label': 0}
+
+# Decode label
+print(ds["train"].features["label"].int2str(0))
+# 'Bank account'
+```
+
+## Associated Model
+
+This dataset was used to fine-tune [WillisBack/modernbert-large-consumer-finance-11cls](https://huggingface.co/WillisBack/modernbert-large-consumer-finance-11cls) — a ModernBERT-large encoder achieving **F1 macro 0.61** and **78.2% accuracy**.
+
+## Citation
+
+```bibtex
+@misc{derue2026cfpb11cls,
+  author = {Derue, William},
+  title = {Consumer Finance Complaints Dataset — 11 Consolidated Labels},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/WillisBack/dataset-financial-user-claim}
+}
+```

test.parquet

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train.parquet

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