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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    TypeError
Message:      Expected a dict or a list but got <class 'NoneType'>: None
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1004, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 605, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 386, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 317, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2031, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2027, in from_yaml_inner
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                                ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2005, in from_yaml_inner
                  _feature = from_yaml_inner(unsimplify(obj).pop(_type))
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2029, in from_yaml_inner
                  raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
              TypeError: Expected a dict or a list but got <class 'NoneType'>: None

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Blended Skill Talk

Dataset Summary

This dataset contains conversations between two personas with additional context previous utterances free messages guided messages suggestions and guided chosen suggestions allowing for the creation of natural multi-modal conversations with personality empathy and knowledge

The conversations are designed to measure a full range of technical competencies such as dialogue flow management including response times topic control and coherence of conversation It also provides a basis for exploring the impact of different conversational styles on user engagement Additionally the dataset is useful for validating distributed dialogue systems across various modalities while revealing potential biases present in different contexts Finally it enables benchmarking against similar datasets toward the development of an automatic evaluation system for assessing tactical skill talk performance over time


Data Structure

Fields

Field Description
personas List of personas participating in the conversation
additional_context Extra context or scenario description
previous_utterance The immediately preceding dialogue turn(s)
context General conversation context
free_messages Free-form user or system messages
guided_messages Messages generated via guided prompts
suggestions Suggested responses from ConvAI2 Empathetic Dialogues and Wizard of Wikipedia
guided_chosen_suggestions Selected suggestions actually used in the conversation
label_candidates Optional candidate labels null in this dataset

Splits

Split Examples Size (bytes) Description
Train 4096 9201244 Used for model training
Validation 723 1629426 Used for validation and tuning

Total dataset size: 10830670 bytes
Total number of dialogues: 4819


Usage Example

from datasets import load_dataset

ds = load_dataset("anezatra/blended-skill-talk", split="train")
print(ds[0])

References

Smith, E. M., Williamson, M., Shuster, K., Weston, J., & Boureau, Y. L. (2020). Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills. arXiv preprint arXiv:2004.08449. (https://arxiv.org/abs/2004.08449)

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