
# stacked-xsum-1024


a "stacked" version of `xsum` 

1. Original Dataset: copy of the base dataset

2. Stacked Rows: The original dataset is processed by stacking rows based on certain criteria:
   - Maximum Input Length: The maximum length for input sequences is 1024 tokens in the longt5 model tokenizer.
   - Maximum Output Length: The maximum length for output sequences is also 1024 tokens in the longt5 model tokenizer.

3. Special Token: The dataset utilizes the `[NEXT_CONCEPT]` token to indicate a new topic **within** the same summary. It is recommended to explicitly add this special token to your model's tokenizer before training, ensuring that it is recognized and processed correctly during downstream usage.
4. 
## updates 

- dec 3: upload initial version
- dec 4: upload v2 with basic data quality fixes (i.e. the `is_stacked` column)
- dec 5 0500: upload v3 which has pre-randomised order and duplicate rows for document+summary dropped


## stats


![stats](https://i.imgur.com/TyyDthT.png)

## dataset details

see the repo `.log` file for more details.

train input

```python
[2022-12-05 01:05:17] INFO:root:INPUTS - basic stats - train
[2022-12-05 01:05:17] INFO:root:{'num_columns': 5,
 'num_rows': 204045,
 'num_unique_target': 203107,
 'num_unique_text': 203846,
 'summary - average chars': 125.46,
 'summary - average tokens': 30.383719277610332,
 'text input - average chars': 2202.42,
 'text input - average tokens': 523.9222230390355}

```

stacked train:

```python
[2022-12-05 04:47:01] INFO:root:stacked 181719 rows, 22326 rows were ineligible
[2022-12-05 04:47:02] INFO:root:dropped 64825 duplicate rows, 320939 rows remain
[2022-12-05 04:47:02] INFO:root:shuffling output with seed 323
[2022-12-05 04:47:03] INFO:root:STACKED - basic stats - train
[2022-12-05 04:47:04] INFO:root:{'num_columns': 6,
 'num_rows': 320939,
 'num_unique_chapters': 320840,
 'num_unique_summaries': 320101,
 'summary - average chars': 199.89,
 'summary - average tokens': 46.29925001324239,
 'text input - average chars': 2629.19,
 'text input - average tokens': 621.541532814647}
```

## Citation

If you find this useful in your work, please consider citing us.

```
@misc {stacked_summaries_2023,
	author       = { {Stacked Summaries: Karim Foda and Peter Szemraj} },
	title        = { stacked-xsum-1024 (Revision 2d47220) },
	year         = 2023,
	url          = { https://huggingface.co/datasets/stacked-summaries/stacked-xsum-1024 },
	doi          = { 10.57967/hf/0390 },
	publisher    = { Hugging Face }
}
```
task_categories:summarization size_categories:100K<n<1M source_datasets:xsum language:en license:apache-2.0 stacked summaries xsum doi:10.57967/hf/0390 region:us
{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "source_datasets": ["xsum"], "task_categories": ["summarization"], "pretty_name": "Stacked XSUM: 1024 tokens max", "tags": ["stacked summaries", "xsum"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "document", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "chapter_length", "dtype": "int64"}, {"name": "summary_length", "dtype": "int64"}, {"name": "is_stacked", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 918588672, "num_examples": 320939}, {"name": "validation", "num_bytes": 51154057, "num_examples": 17935}, {"name": "test", "num_bytes": 51118088, "num_examples": 17830}], "download_size": 653378162, "dataset_size": 1020860817}}
TAGS
#task_categories-summarization #size_categories-100K<n<1M #source_datasets-xsum #language-English #license-apache-2.0 #stacked summaries #xsum #doi-10.57967/hf/0390 #region-us 


# stacked-xsum-1024


a "stacked" version of 'xsum' 

1. Original Dataset: copy of the base dataset

2. Stacked Rows: The original dataset is processed by stacking rows based on certain criteria:
   - Maximum Input Length: The maximum length for input sequences is 1024 tokens in the longt5 model tokenizer.
   - Maximum Output Length: The maximum length for output sequences is also 1024 tokens in the longt5 model tokenizer.

3. Special Token: The dataset utilizes the '[NEXT_CONCEPT]' token to indicate a new topic within the same summary. It is recommended to explicitly add this special token to your model's tokenizer before training, ensuring that it is recognized and processed correctly during downstream usage.
4. 
## updates 

- dec 3: upload initial version
- dec 4: upload v2 with basic data quality fixes (i.e. the 'is_stacked' column)
- dec 5 0500: upload v3 which has pre-randomised order and duplicate rows for document+summary dropped


## stats


!stats

## dataset details

see the repo '.log' file for more details.

train input



stacked train:



If you find this useful in your work, please consider citing us.


# stacked-xsum-1024


a "stacked" version of 'xsum' 

1. Original Dataset: copy of the base dataset

2. Stacked Rows: The original dataset is processed by stacking rows based on certain criteria:
   - Maximum Input Length: The maximum length for input sequences is 1024 tokens in the longt5 model tokenizer.
   - Maximum Output Length: The maximum length for output sequences is also 1024 tokens in the longt5 model tokenizer.

3. Special Token: The dataset utilizes the '[NEXT_CONCEPT]' token to indicate a new topic within the same summary. It is recommended to explicitly add this special token to your model's tokenizer before training, ensuring that it is recognized and processed correctly during downstream usage.
4. ## updates 

- dec 3: upload initial version
- dec 4: upload v2 with basic data quality fixes (i.e. the 'is_stacked' column)
- dec 5 0500: upload v3 which has pre-randomised order and duplicate rows for document+summary dropped ## stats


!stats ## dataset details

see the repo '.log' file for more details.

train input



stacked train:



If you find this useful in your work, please consider citing us.
TAGS
#task_categories-summarization #size_categories-100K<n<1M #source_datasets-xsum #language-English #license-apache-2.0 #stacked summaries #xsum #doi-10.57967/hf/0390 #region-us 
 # stacked-xsum-1024


a "stacked" version of 'xsum' 

1. Original Dataset: copy of the base dataset

2. Stacked Rows: The original dataset is processed by stacking rows based on certain criteria:
   - Maximum Input Length: The maximum length for input sequences is 1024 tokens in the longt5 model tokenizer.
   - Maximum Output Length: The maximum length for output sequences is also 1024 tokens in the longt5 model tokenizer.

3. Special Token: The dataset utilizes the '[NEXT_CONCEPT]' token to indicate a new topic within the same summary. It is recommended to explicitly add this special token to your model's tokenizer before training, ensuring that it is recognized and processed correctly during downstream usage.
4. ## updates 

- dec 3: upload initial version
- dec 4: upload v2 with basic data quality fixes (i.e. the 'is_stacked' column)
- dec 5 0500: upload v3 which has pre-randomised order and duplicate rows for document+summary dropped ## stats


!stats ## dataset details

see the repo '.log' file for more details.

train input



stacked train:



If you find this useful in your work, please consider citing us.
passage: TAGS
#task_categories-summarization #size_categories-100K<n<1M #source_datasets-xsum #language-English #license-apache-2.0 #stacked summaries #xsum #doi-10.57967/hf/0390 #region-us 
# stacked-xsum-1024


a "stacked" version of 'xsum' 

1. Original Dataset: copy of the base dataset

2. Stacked Rows: The original dataset is processed by stacking rows based on certain criteria:
   - Maximum Input Length: The maximum length for input sequences is 1024 tokens in the longt5 model tokenizer.
   - Maximum Output Length: The maximum length for output sequences is also 1024 tokens in the longt5 model tokenizer.

3. Special Token: The dataset utilizes the '[NEXT_CONCEPT]' token to indicate a new topic within the same summary. It is recommended to explicitly add this special token to your model's tokenizer before training, ensuring that it is recognized and processed correctly during downstream usage.
4.## updates 

- dec 3: upload initial version
- dec 4: upload v2 with basic data quality fixes (i.e. the 'is_stacked' column)
- dec 5 0500: upload v3 which has pre-randomised order and duplicate rows for document+summary dropped## stats


!stats## dataset details

see the repo '.log' file for more details.

train input



stacked train:



If you find this useful in your work, please consider citing us.