# TypeError: Expected feature type 'Value(dtype='int32')' but got 'Value(dtype='int64')' for column 'labels'. Cast the column to the correct dtype.

- **ID:** `huggingface/dataset-feature-type-mismatch`
- **Domain:** huggingface
- **Category:** type_error
- **Verification:** ai_generated
- **Fix Rate:** 88%

## Root Cause

The dataset schema defines a specific dtype for a column (e.g., int32) but the actual data loaded has a different dtype (e.g., int64), causing a mismatch during batching or model input preparation.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| datasets>=2.16.0 | active | — | — |
| transformers>=4.35.0 | active | — | — |
| Python>=3.8 | active | — | — |

## Workarounds

1. **Cast the column to the expected dtype using Dataset.cast_column:
from datasets import load_dataset, Features, Value
dataset = load_dataset('org/dataset', split='train')
expected_features = Features({'labels': Value('int32')})
dataset = dataset.cast(expected_features)
# Or cast a single column:
dataset = dataset.cast_column('labels', Value('int32'))** (88% success)
   ```
   Cast the column to the expected dtype using Dataset.cast_column:
from datasets import load_dataset, Features, Value
dataset = load_dataset('org/dataset', split='train')
expected_features = Features({'labels': Value('int32')})
dataset = dataset.cast(expected_features)
# Or cast a single column:
dataset = dataset.cast_column('labels', Value('int32'))
   ```
2. **Use Dataset.map to manually convert the column:
def convert_labels(example):
    example['labels'] = int(example['labels'])  # Python int is flexible
    return example
dataset = dataset.map(convert_labels)
# Then let the data collator handle casting automatically.** (82% success)
   ```
   Use Dataset.map to manually convert the column:
def convert_labels(example):
    example['labels'] = int(example['labels'])  # Python int is flexible
    return example
dataset = dataset.map(convert_labels)
# Then let the data collator handle casting automatically.
   ```

## Dead Ends

- **** — Ignoring the error and using the dataset as-is may cause silent casting during training, leading to memory inefficiency or runtime errors in PyTorch. (70% fail)
- **** — Dropping the column with remove_columns removes the feature entirely, causing a missing column error later. (80% fail)