The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ValueError
Message: Not able to read records in the JSON file at hf://datasets/bobotran/spoiler-matching@f10e71b6212adbe80c82ea240c18c831d36100f0/matching/handlabeled_only/train.json.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 241, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head
return _examples_to_batch(list(self.take(n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__
yield from islice(self.ex_iterable, self.n)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
for key, pa_table in self.generate_tables_fn(**self.kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 165, in _generate_tables
raise ValueError(f"Not able to read records in the JSON file at {file}.") from None
ValueError: Not able to read records in the JSON file at hf://datasets/bobotran/spoiler-matching@f10e71b6212adbe80c82ea240c18c831d36100f0/matching/handlabeled_only/train.json.Need help to make the dataset viewer work? Open a discussion for direct support.
Episode-Specific Spoilers
This is the spoiler matching dataset as presented in Spoiler Detection as Semantic Text Matching. It consists of comments discussing episodes from various TV shows. Unlike other spoiler datasets, this dataset assigns an episode number (and show name) to each comment, enabling matching to specific episodes and very fine-grain spoiler detection. This dataset also includes an episode summary for each (show, episode) pair. For a given show, the task is to rank the summaries for each comment.
Usage
See the Spoiler Matching repository for examples on how to train a spoiler matching model on this dataset.
Annotation
522,991 comments from 13 TV shows were scraped from episode discussion threads on Reddit. Of these comments, some are actually relevant to their respective episode discussion and others are irrelevant. A subset of these comments (11,032) were hand-labeled as irrelevant or relevant. This hand-labeled dataset was used to train an irrelevant/relevant classifier which auto-labeled the remaining comments. All relevant comments were formatted into the matching dataset.
Details
The matching folder contains the spoiler matching dataset, and the filtering folder contains intermediate data from the auto-labeling step.
matching/
This folder contains the datasets for training spoiler matching models. All comments in these files were determined to be relevant, whether that was done by a human annotator or the auto-labeler. summaries.json contains the summary for each episode. summaries.json and test.json are the same across with_autolabels and handlabeled_only.
matching/with_autolabels/
The with_autolabels folder contains the main dataset. test.json and val.json consist of hand-labeled relevants while train.json contains auto-labeled relevants. To measure the performance of spoiler matching models on unseen shows, test.json was constructed such that it consists of comments from 4 TV shows which are neither present in val.json nor train.json.
matching/handlabeled_only/
The handlabeled_only folder shares the same test.json with matching/with_autolabels/, but train.json and val.json are split 80-20 from matching/with_autolabels/val.json respectively.
filtering/
This folder contains data from the auto-labeling step.
filtering/handlabeled
This folder contains the dataset used to train the autolabeler. Comments with a 1 in the first column were hand-labeled as irrelevant. Comments with a 0 in the first column were hand-labeled as relevant. The last two columns are the show name and episode number respectively, which are not used during this step.
filtering/unlabeled
The unlabeled comments were split into two chunks to make them more manageable to load into memory during inference. All comments have a -1 in the first column to represent that they are unlabeled.
- Downloads last month
- 0