Repocard metadata
This PR adds a metadata_update function that allows the user to update the metadata in a repository on the hub. The function accepts a dict with metadata (following the same pattern as the YAML in the README) and behaves as follows for all top level fields except model-index.
Examples:
Starting from
existing_results = [{
'dataset': {'name': 'IMDb', 'type': 'imdb'},
'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.995}],
'task': {'name': 'Text Classification', 'type': 'text-classification'}
}]
1. Overwrite existing metric value in existing result
new_results = deepcopy(existing_results)
new_results[0]["metrics"][0]["value"] = 0.999
_update_metadata_model_index(existing_results, new_results, overwrite=True)
[{'dataset': {'name': 'IMDb', 'type': 'imdb'},
'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.999}],
'task': {'name': 'Text Classification', 'type': 'text-classification'}}]
2. Add new metric to existing result
new_results = deepcopy(existing_results)
new_results[0]["metrics"][0]["name"] = "Recall"
new_results[0]["metrics"][0]["type"] = "recall"
[{'dataset': {'name': 'IMDb', 'type': 'imdb'},
'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.995},
{'name': 'Recall', 'type': 'recall', 'value': 0.995}],
'task': {'name': 'Text Classification', 'type': 'text-classification'}}]
3. Add new result
new_results = deepcopy(existing_results)
new_results[0]["dataset"] = {'name': 'IMDb-2', 'type': 'imdb_2'}
[{'dataset': {'name': 'IMDb', 'type': 'imdb'},
'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.995}],
'task': {'name': 'Text Classification', 'type': 'text-classification'}},
{'dataset': ({'name': 'IMDb-2', 'type': 'imdb_2'},),
'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.995}],
'task': {'name': 'Text Classification', 'type': 'text-classification'}}]
Improvements and bug fixes
- Keras: Saving history in a JSON file by @merveenoyan in #861
- space after uri by @leondz in #866