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# nemo_automodel.components.datasets.llm.seq_cls

## Module Contents

### Classes

| Name                                                                                 | Description                                             |
| ------------------------------------------------------------------------------------ | ------------------------------------------------------- |
| [`GLUE_MRPC`](#nemo_automodel-components-datasets-llm-seq_cls-GLUE_MRPC)             | GLUE MRPC dataset (sentence pair classification).       |
| [`GLUE_MRPCConfig`](#nemo_automodel-components-datasets-llm-seq_cls-GLUE_MRPCConfig) | Construction-time configuration for :class:`GLUE_MRPC`. |

### API

```python
class nemo_automodel.components.datasets.llm.seq_cls.GLUE_MRPC(
    tokenizer,
    split: str = 'train',
    num_samples_limit: typing.Optional[int] = None,
    trust_remote_code: bool = True,
    max_length: typing.Optional[int] = 256
)
```

GLUE MRPC dataset (sentence pair classification).

Produces tokenized inputs with both sentence1 and sentence2 using the provided tokenizer.

```python
nemo_automodel.components.datasets.llm.seq_cls.GLUE_MRPC.__getitem__(
    idx
)
```

```python
nemo_automodel.components.datasets.llm.seq_cls.GLUE_MRPC.__len__()
```

```python
class nemo_automodel.components.datasets.llm.seq_cls.GLUE_MRPCConfig(
    split: str = 'train',
    num_samples_limit: int | None = None,
    trust_remote_code: bool = True,
    max_length: int | None = 256
)
```

Dataclass

Construction-time configuration for :class:`GLUE_MRPC`.

Maximum tokenization length (`None` derives one from the tokenizer).

If set, limit the dataset to this many samples via slicing.

Dataset split to use (e.g. `train`, `validation`).

Forwarded to `datasets.load_dataset`.

```python
nemo_automodel.components.datasets.llm.seq_cls.GLUE_MRPCConfig.build(
    tokenizer: 'PreTrainedTokenizerBase | None'
) -> 'GLUE_MRPC'
```

Build a :class:`GLUE_MRPC` dataset from this :class:`GLUE_MRPCConfig` and a runtime tokenizer.