Datasets:

Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
albertvillanova HF staff commited on
Commit
f817a2b
1 Parent(s): f295b0d

Convert dataset to Parquet

Browse files

Convert dataset to Parquet.

README.md CHANGED
@@ -33,16 +33,25 @@ dataset_info:
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  '1': positive
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  splits:
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  - name: train
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- num_bytes: 4690022
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  num_examples: 67349
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  - name: validation
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- num_bytes: 106361
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  num_examples: 872
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  - name: test
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- num_bytes: 216868
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  num_examples: 1821
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- download_size: 7439277
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- dataset_size: 5013251
 
 
 
 
 
 
 
 
 
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  ---
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  # Dataset Card for [Dataset Name]
 
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  '1': positive
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  splits:
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  - name: train
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+ num_bytes: 4681603
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  num_examples: 67349
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  - name: validation
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  num_examples: 872
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  - name: test
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  num_examples: 1821
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+ download_size: 3331058
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+ dataset_size: 5004495
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/train-*
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+ - split: validation
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+ path: data/validation-*
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+ - split: test
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+ path: data/test-*
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  ---
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  # Dataset Card for [Dataset Name]
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dataset_infos.json CHANGED
@@ -1 +1,57 @@
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- {"default": {"description": "The Stanford Sentiment Treebank consists of sentences from movie reviews and\nhuman annotations of their sentiment. The task is to predict the sentiment of a\ngiven sentence. We use the two-way (positive/negative) class split, and use only\nsentence-level labels.\n", "citation": "@inproceedings{socher2013recursive,\n title={Recursive deep models for semantic compositionality over a sentiment treebank},\n author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},\n booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},\n pages={1631--1642},\n year={2013}\n}\n", "homepage": "https://nlp.stanford.edu/sentiment/", "license": "Unknown", "features": {"idx": {"dtype": "int32", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["negative", "positive"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "sst2", "config_name": "default", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4690022, "num_examples": 67349, "dataset_name": "sst2"}, "validation": {"name": "validation", "num_bytes": 106361, "num_examples": 872, "dataset_name": "sst2"}, "test": {"name": "test", "num_bytes": 216868, "num_examples": 1821, "dataset_name": "sst2"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/SST-2.zip": {"num_bytes": 7439277, "checksum": "d67e16fb55739c1b32cdce9877596db1c127dc322d93c082281f64057c16deaa"}}, "download_size": 7439277, "post_processing_size": null, "dataset_size": 5013251, "size_in_bytes": 12452528}}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "default": {
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+ "description": "The Stanford Sentiment Treebank consists of sentences from movie reviews and\nhuman annotations of their sentiment. The task is to predict the sentiment of a\ngiven sentence. We use the two-way (positive/negative) class split, and use only\nsentence-level labels.\n",
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+ "citation": "@inproceedings{socher2013recursive,\n title={Recursive deep models for semantic compositionality over a sentiment treebank},\n author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},\n booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},\n pages={1631--1642},\n year={2013}\n}\n",
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