PylateModelCardData

A dataclass for storing data used in the model card.

Parameters

• language ('str | list[str] | None') – defaults to <factory>

  The model language, either a string or a list of strings, e.g., "en" or ["en", "de", "nl"].

• license ('str | None') – defaults to None

  The license of the model, e.g., "apache-2.0", "mit", or "cc-by-nc-sa-4.0".

• model_name ('str | None') – defaults to None

  The pretty name of the model, e.g., "SentenceTransformer based on microsoft/mpnet-base".

• model_id ('str | None') – defaults to None

  The model ID for pushing the model to the Hub, e.g., "tomaarsen/sbert-mpnet-base-allnli".

• train_datasets ('list[dict[str, str]]') – defaults to <factory>

  A list of dictionaries containing names and/or Hugging Face dataset IDs for training datasets, e.g., [{"name": "SNLI", "id": "stanfordnlp/snli"}, {"name": "MultiNLI", "id": "nyu-mll/multi_nli"}, {"name": "STSB"}].

• eval_datasets ('list[dict[str, str]]') – defaults to <factory>

  A list of dictionaries containing names and/or Hugging Face dataset IDs for evaluation datasets, e.g., [{"name": "SNLI", "id": "stanfordnlp/snli"}, {"id": "mteb/stsbenchmark-sts"}].

• task_name ('str') – defaults to semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more

  The human-readable task the model is trained on, e.g., "semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more".

• tags ('list[str] | None') – defaults to <factory>

  A list of tags for the model, e.g., ["sentence-transformers", "sentence-similarity", "feature-extraction"].

• generate_widget_examples ("Literal['deprecated']") – defaults to deprecated

Attributes

• base_model

• base_model_revision

• best_model_step

• code_carbon_callback

• license

• model

• model_id

• model_name

• predict_example

• trainer

Methods

add_tags

compute_dataset_metrics

Given a dataset, compute the following: * Dataset Size * Dataset Columns * Dataset Stats - Strings: min, mean, max word count/token length - Integers: Counter() instance - Floats: min, mean, max range - List: number of elements or min, mean, max number of elements * 3 Example samples * Loss function name - Loss function config

Parameters

• dataset ('Dataset | IterableDataset | None')
• dataset_info ('dict[str, Any]')
• loss ('dict[str, nn.Module] | nn.Module | None')

extract_dataset_metadata

format_eval_metrics

Format the evaluation metrics for the model card.

The following keys will be returned: - eval_metrics: A list of dictionaries containing the class name, description, dataset name, and a markdown table This is used to display the evaluation metrics in the model card. - metrics: A list of all metric keys. This is used in the model card metadata. - model-index: A list of dictionaries containing the task name, task type, dataset type, dataset name, metric name, metric type, and metric value. This is used to display the evaluation metrics in the model card metadata.

format_training_logs

get

Get value for a given metadata key.

Parameters

• key (str)
• default (Any) – defaults to None

get_codecarbon_data

infer_datasets

pop

Pop value for a given metadata key.

Parameters

• key (str)
• default (Any) – defaults to None

register_model

set_base_model

set_best_model_step

set_evaluation_metrics

set_label_examples

set_language

set_license

set_losses

set_model_id

set_widget_examples

to_dict

Converts CardData to a dict.

Returns: dict: CardData represented as a dictionary ready to be dumped to a YAML block for inclusion in a README.md file.

to_yaml

Dumps CardData to a YAML block for inclusion in a README.md file.

Args: line_break (str, optional): The line break to use when dumping to yaml. Returns: str: CardData represented as a YAML block.

Parameters

• line_break – defaults to None

try_to_set_base_model

validate_datasets