Add 2.1.2 version with cased raw strings (#3)

- Add 2.1.2 data files (04f83833dba762753c1c4d12c5cff6e6e4926b96)
- Move 1.0.0 data files (074aeed8e1a0d039c13b171a77a67950f71c5496)
- Update dataset loading script (0f27e656ac5751add50114c74468dfb433fdec30)
- Update instructions about version in dataset card (a815ad4f6cfd2bc49b15b3c734a8e29f8fe336c6)
- Update metadata in dataset card (30ea432618e70552803977936d6854c9b03c1433)
- Update metadata in legacy JSON (ef871d08aeb1fe2293f40380298f7f2071f485a4)

README.md

@@ -42,16 +42,16 @@ dataset_info:
     dtype: string
   splits:
   - name: train
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+    num_bytes: 38367048389
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   - name: validation
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     num_examples: 67068
   - name: test
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+    num_bytes: 2129505280
     num_examples: 67072
-  download_size: 6447221554
-  dataset_size: 25949907528
+  download_size: 10142923776
+  dataset_size: 42612380671
 - config_name: a
   features:
   - name: description
@@ -60,16 +60,16 @@ dataset_info:
     dtype: string
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   - name: train
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+    num_bytes: 5683460620
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   - name: validation
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     num_examples: 9674
   - name: test
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+    num_bytes: 316633277
     num_examples: 9675
-  download_size: 6447221554
-  dataset_size: 3699882485
+  download_size: 10142923776
+  dataset_size: 6313418402
 - config_name: b
   features:
   - name: description
@@ -78,16 +78,16 @@ dataset_info:
     dtype: string
   splits:
   - name: train
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+    num_bytes: 4236070976
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   - name: validation
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     num_examples: 8973
   - name: test
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+    num_bytes: 231538734
     num_examples: 8974
-  download_size: 6447221554
-  dataset_size: 2858365068
+  download_size: 10142923776
+  dataset_size: 4702034848
 - config_name: c
   features:
   - name: description
@@ -96,16 +96,16 @@ dataset_info:
     dtype: string
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   - name: validation
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   - name: test
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+    num_bytes: 252566793
     num_examples: 5614
-  download_size: 6447221554
-  dataset_size: 2936467229
+  download_size: 10142923776
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 - config_name: d
   features:
   - name: description
@@ -114,16 +114,16 @@ dataset_info:
     dtype: string
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   - name: validation
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     num_examples: 565
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-  download_size: 6447221554
-  dataset_size: 177848844
+  download_size: 10142923776
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 - config_name: e
   features:
   - name: description
@@ -132,16 +132,16 @@ dataset_info:
     dtype: string
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     num_examples: 1914
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+    num_bytes: 48586429
     num_examples: 1914
-  download_size: 6447221554
-  dataset_size: 593960836
+  download_size: 10142923776
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 - config_name: f
   features:
   - name: description
@@ -150,16 +150,16 @@ dataset_info:
     dtype: string
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   - name: validation
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-  download_size: 6447221554
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+  download_size: 10142923776
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 - config_name: g
   features:
   - name: description
@@ -168,16 +168,16 @@ dataset_info:
     dtype: string
   splits:
   - name: train
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+    num_bytes: 8877854206
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   - name: validation
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     num_examples: 14385
   - name: test
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     num_examples: 14386
-  download_size: 6447221554
-  dataset_size: 6190993271
+  download_size: 10142923776
+  dataset_size: 9866760236
 - config_name: h
   features:
   - name: description
@@ -186,16 +186,16 @@ dataset_info:
     dtype: string
   splits:
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     num_examples: 257019
   - name: validation
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   - name: test
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     num_examples: 14279
-  download_size: 6447221554
-  dataset_size: 5538164212
+  download_size: 10142923776
+  dataset_size: 8968684827
 - config_name: y
   features:
   - name: description
@@ -204,16 +204,16 @@ dataset_info:
     dtype: string
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   - name: validation
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   - name: test
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+    num_bytes: 204394948
     num_examples: 6911
-  download_size: 6447221554
-  dataset_size: 2512474776
+  download_size: 10142923776
+  dataset_size: 4100353733
 ---
 
 # Dataset Card for Big Patent
@@ -252,17 +252,36 @@ dataset_info:
 
 ### Dataset Summary
 
-BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Each US patent application is filed under a Cooperative Patent Classification (CPC) code. There are nine such classification categories:
+BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries.
+Each US patent application is filed under a Cooperative Patent Classification (CPC) code.
+There are nine such classification categories:
+- a: Human Necessities
+- b: Performing Operations; Transporting
+- c: Chemistry; Metallurgy
+- d: Textiles; Paper
+- e: Fixed Constructions
+- f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting
+- g: Physics
+- h: Electricity
+- y: General tagging of new or cross-sectional technology
+
+Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes:
+```python
+from datasets import load_dataset
+
+ds = load_dataset("big_patent")  # default is 'all' CPC codes
+ds = load_dataset("big_patent", "all")  # the same as above
+ds = load_dataset("big_patent", "a")  # only 'a' CPC codes
+ds = load_dataset("big_patent", codes=["a", "b"])
+```
+
+To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`:
+```python
+ds = load_dataset("big_patent", codes="all", version="1.0.0")
+ds = load_dataset("big_patent", codes="a", version="1.0.0")
+ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0")
+```
 
-- A (Human Necessities)
-- B (Performing Operations; Transporting)
-- C (Chemistry; Metallurgy)
-- D (Textiles; Paper)
-- E (Fixed Constructions)
-- F (Mechanical Engineering; Lightning; Heating; Weapons; Blasting)
-- G (Physics)
-- H (Electricity)
-- Y (General tagging of new or cross-sectional technology)
 
 ### Supported Tasks and Leaderboards
 
@@ -277,6 +296,12 @@ English
 ### Data Instances
 
 Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section.
+```
+{
+  'description': 'FIELD OF THE INVENTION  \n       [0001]     This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...',
+  'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...'
+}
+```
 
 ### Data Fields
 

big_patent.py

@@ -15,7 +15,6 @@
 
 # Lint as: python3
 """BigPatent Dataset."""
-import glob
 import gzip
 import json
 import os
@@ -53,12 +52,8 @@ There are two features:
 
 _LICENSE = "Creative Commons Attribution 4.0 International"
 
-_REPO = "https://huggingface.co/datasets/big_patent/resolve/main/data"
-_URLS = {
-    "train": f"{_REPO}/train.zip",
-    "validation": f"{_REPO}/val.zip",
-    "test": f"{_REPO}/test.zip",
-}
+_SPLIT_NAMES = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "val", datasets.Split.TEST: "test"}
+_URL = "data/{version}/{split_name}.zip"
 
 _DOCUMENT = "description"
 _SUMMARY = "abstract"
@@ -78,38 +73,44 @@ _CPC_DESCRIPTION = {
 # Available versions:
 # 1.0.0 lower cased tokenized words.
 # 2.0.0 cased raw strings.
-# 2.1.0 cased raw strings (fixed).
-# TODO Add raw string versions
+# 2.1.2 cased raw strings (fixed).
 
-_VERSION = "1.0.0"
+_VERSION = "2.1.2"
 
 
 class BigPatentConfig(datasets.BuilderConfig):
     """BuilderConfig for BigPatent."""
 
-    def __init__(self, *args, cpc_codes=None, **kwargs):
+    def __init__(self, codes="all", version=_VERSION, **kwargs):
         """BuilderConfig for BigPatent.
         Args:
-            cpc_codes: str, cpc_codes
+            codes (str or list, default 'all'): CPC codes. Either 'all' or a combination
+                of {'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'y'}.
             **kwargs: keyword arguments forwarded to super.
         """
-        super().__init__(*args, version=_VERSION, **kwargs)
-        self.cpc_codes = cpc_codes
+        if isinstance(codes, str):
+            codes = [codes]
+        name = "+".join(codes)
+        if name == "all":
+            codes = list(_CPC_DESCRIPTION)
+        if version != _VERSION:
+            name = f"{name}-{version}"
+        super().__init__(name=name, version=version, **kwargs)
+        self.codes = codes
 
 
 class BigPatent(datasets.GeneratorBasedBuilder):
     """BigPatent datasets."""
 
+    BUILDER_CONFIG_CLASS = BigPatentConfig
     BUILDER_CONFIGS = [
         BigPatentConfig(
-            cpc_codes=list(_CPC_DESCRIPTION),
-            name="all",
+            codes="all",
             description="Patents under all categories.",
         ),
     ] + [
-        BigPatentConfig(  # pylint:disable=g-complex-comprehension
-            cpc_codes=[k],
-            name=k,
+        BigPatentConfig(
+            codes=k,
             description=f"Patents under Cooperative Patent Classification (CPC) {k}: {v}",
         )
         for k, v in sorted(_CPC_DESCRIPTION.items())
@@ -129,22 +130,30 @@ class BigPatent(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        dl_paths = dl_manager.download_and_extract(_URLS)
-        split_dirs = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "val", datasets.Split.TEST: "test"}
+        urls = {
+            split: _URL.format(version=self.config.version, split_name=split_name)
+            for split, split_name in _SPLIT_NAMES.items()
+        }
+        dl_paths = dl_manager.download_and_extract(urls)
+        paths = {
+            split: [
+                dl_manager.iter_files(os.path.join(dl_paths[split], split_name, code)) for code in self.config.codes
+            ]
+            for split, split_name in _SPLIT_NAMES.items()
+        }
         return [
             datasets.SplitGenerator(
                 name=split,
-                gen_kwargs={"path": dl_paths[split], "split_dir": split_dirs[split]},
+                gen_kwargs={"paths": paths[split]},
             )
-            for split in split_dirs
+            for split in _SPLIT_NAMES
         ]
 
-    def _generate_examples(self, path=None, split_dir=None):
+    def _generate_examples(self, paths=None):
         """Yields examples."""
-        for cpc_code in self.config.cpc_codes:
-            filenames = glob.glob(os.path.join(path, split_dir, cpc_code, "*"))
-            for filename in sorted(filenames):
-                with open(filename, "rb") as fin:
+        for paths_per_code in paths:
+            for path in paths_per_code:
+                with open(path, "rb") as fin:
                     fin = gzip.GzipFile(fileobj=fin)
                     for row in fin:
                         json_obj = json.loads(row)

data/2.1.2/test.zip

@@ -0,0 +1,3 @@
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+oid sha256:614821d8d8c92451ba495e0b381f157a0e0b36977a70f2d4cb3ae4b6b706539a
+size 507619065

data/2.1.2/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:59cae1439b01102faa5b9e1bb3061867ba292a6093cf66ee3d1bc33030d7a91f
+size 9129045386

data/2.1.2/val.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9206de008607095bdb29f405ef9b525565556860d015cbd4261154644b51ce9b
+size 506259325

dataset_infos.json

@@ -1 +1 @@
-{"all": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - abstract: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "Creative Commons Attribution 4.0 International", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "task_templates": null, "builder_name": "big_patent", "config_name": "all", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 23363518650, "num_examples": 1207222, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 1290154487, "num_examples": 67068, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 1296234391, "num_examples": 67072, "dataset_name": "big_patent"}}, "download_checksums": {"https://huggingface.co/datasets/big_patent/resolve/main/data/train.zip": {"num_bytes": 5802341237, "checksum": "89831e047474822ff7be521707e9a58bd9acd4d359a194cc564e74012a44185d"}, "https://huggingface.co/datasets/big_patent/resolve/main/data/val.zip": {"num_bytes": 321731760, "checksum": "c8a3c745cac4216bd32a54149dbc044ea397e405a08b230041a554e3eb75080d"}, "https://huggingface.co/datasets/big_patent/resolve/main/data/test.zip": {"num_bytes": 323148557, "checksum": "ce605cafb69b1757276326c51d18a1c09275b1911252dd95e8e0dcbf386d1b77"}}, "download_size": 6447221554, "post_processing_size": null, "dataset_size": 25949907528, "size_in_bytes": 32397129082}, "a": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - abstract: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "Creative Commons Attribution 4.0 International", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "task_templates": null, "builder_name": "big_patent", "config_name": "a", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3329778447, "num_examples": 174134, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 184116486, "num_examples": 9674, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 185987552, "num_examples": 9675, "dataset_name": "big_patent"}}, "download_checksums": {"https://huggingface.co/datasets/big_patent/resolve/main/data/train.zip": {"num_bytes": 5802341237, "checksum": "89831e047474822ff7be521707e9a58bd9acd4d359a194cc564e74012a44185d"}, "https://huggingface.co/datasets/big_patent/resolve/main/data/val.zip": {"num_bytes": 321731760, "checksum": "c8a3c745cac4216bd32a54149dbc044ea397e405a08b230041a554e3eb75080d"}, "https://huggingface.co/datasets/big_patent/resolve/main/data/test.zip": {"num_bytes": 323148557, "checksum": "ce605cafb69b1757276326c51d18a1c09275b1911252dd95e8e0dcbf386d1b77"}}, "download_size": 6447221554, "post_processing_size": null, "dataset_size": 3699882485, "size_in_bytes": 10147104039}, "b": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - abstract: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "Creative Commons Attribution 4.0 International", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "task_templates": null, "builder_name": "big_patent", "config_name": "b", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2574594655, "num_examples": 161520, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 143029380, "num_examples": 8973, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 140741033, "num_examples": 8974, "dataset_name": "big_patent"}}, "download_checksums": {"https://huggingface.co/datasets/big_patent/resolve/main/data/train.zip": {"num_bytes": 5802341237, "checksum": "89831e047474822ff7be521707e9a58bd9acd4d359a194cc564e74012a44185d"}, "https://huggingface.co/datasets/big_patent/resolve/main/data/val.zip": {"num_bytes": 321731760, "checksum": "c8a3c745cac4216bd32a54149dbc044ea397e405a08b230041a554e3eb75080d"}, "https://huggingface.co/datasets/big_patent/resolve/main/data/test.zip": {"num_bytes": 323148557, "checksum": "ce605cafb69b1757276326c51d18a1c09275b1911252dd95e8e0dcbf386d1b77"}}, "download_size": 6447221554, "post_processing_size": null, "dataset_size": 2858365068, "size_in_bytes": 9305586622}, "c": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - abstract: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "Creative Commons Attribution 4.0 International", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "task_templates": null, "builder_name": "big_patent", "config_name": "c", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2641973267, "num_examples": 101042, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 145441704, "num_examples": 5613, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 149052258, "num_examples": 5614, "dataset_name": "big_patent"}}, "download_checksums": {"https://huggingface.co/datasets/big_patent/resolve/main/data/train.zip": {"num_bytes": 5802341237, "checksum": "89831e047474822ff7be521707e9a58bd9acd4d359a194cc564e74012a44185d"}, "https://huggingface.co/datasets/big_patent/resolve/main/data/val.zip": {"num_bytes": 321731760, "checksum": "c8a3c745cac4216bd32a54149dbc044ea397e405a08b230041a554e3eb75080d"}, "https://huggingface.co/datasets/big_patent/resolve/main/data/test.zip": {"num_bytes": 323148557, "checksum": "ce605cafb69b1757276326c51d18a1c09275b1911252dd95e8e0dcbf386d1b77"}}, "download_size": 6447221554, "post_processing_size": null, "dataset_size": 2936467229, "size_in_bytes": 9383688783}, "d": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - abstract: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "Creative Commons Attribution 4.0 International", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "task_templates": null, "builder_name": "big_patent", "config_name": "d", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 160467163, "num_examples": 10164, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 8667961, "num_examples": 565, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 8713720, "num_examples": 565, "dataset_name": "big_patent"}}, "download_checksums": {"https://huggingface.co/datasets/big_patent/resolve/main/data/train.zip": {"num_bytes": 5802341237, "checksum": "89831e047474822ff7be521707e9a58bd9acd4d359a194cc564e74012a44185d"}, "https://huggingface.co/datasets/big_patent/resolve/main/data/val.zip": {"num_bytes": 321731760, "checksum": "c8a3c745cac4216bd32a54149dbc044ea397e405a08b230041a554e3eb75080d"}, "https://huggingface.co/datasets/big_patent/resolve/main/data/test.zip": {"num_bytes": 323148557, "checksum": "ce605cafb69b1757276326c51d18a1c09275b1911252dd95e8e0dcbf386d1b77"}}, "download_size": 6447221554, "post_processing_size": null, "dataset_size": 177848844, "size_in_bytes": 6625070398}, "e": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. 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There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - abstract: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "Creative Commons Attribution 4.0 International", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "task_templates": null, "builder_name": "big_patent", "config_name": "g", "version": "2.1.2", "splits": {"train": {"name": "train", "num_bytes": 8877854206, "num_examples": 258935, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 492581177, "num_examples": 14385, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 496324853, "num_examples": 14386, "dataset_name": "big_patent"}}, "download_checksums": {"data/2.1.2/train.zip": {"num_bytes": 9129045386, "checksum": "59cae1439b01102faa5b9e1bb3061867ba292a6093cf66ee3d1bc33030d7a91f"}, "data/2.1.2/val.zip": {"num_bytes": 506259325, "checksum": "9206de008607095bdb29f405ef9b525565556860d015cbd4261154644b51ce9b"}, "data/2.1.2/test.zip": {"num_bytes": 507619065, "checksum": "614821d8d8c92451ba495e0b381f157a0e0b36977a70f2d4cb3ae4b6b706539a"}}, "download_size": 10142923776, "post_processing_size": null, "dataset_size": 9866760236, "size_in_bytes": 20009684012}, "h": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - abstract: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "Creative Commons Attribution 4.0 International", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "task_templates": null, "builder_name": "big_patent", "config_name": "h", "version": "2.1.2", "splits": {"train": {"name": "train", "num_bytes": 8075621958, "num_examples": 257019, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 447602356, "num_examples": 14279, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 445460513, "num_examples": 14279, "dataset_name": "big_patent"}}, "download_checksums": {"data/2.1.2/train.zip": {"num_bytes": 9129045386, "checksum": "59cae1439b01102faa5b9e1bb3061867ba292a6093cf66ee3d1bc33030d7a91f"}, "data/2.1.2/val.zip": {"num_bytes": 506259325, "checksum": "9206de008607095bdb29f405ef9b525565556860d015cbd4261154644b51ce9b"}, "data/2.1.2/test.zip": {"num_bytes": 507619065, "checksum": "614821d8d8c92451ba495e0b381f157a0e0b36977a70f2d4cb3ae4b6b706539a"}}, "download_size": 10142923776, "post_processing_size": null, "dataset_size": 8968684827, "size_in_bytes": 19111608603}, "y": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n  - description: detailed description of patent.\n  - abstract: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n    title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n    author={Eva Sharma and Chen Li and Lu Wang},\n    year={2019},\n    eprint={1906.03741},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "Creative Commons Attribution 4.0 International", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "task_templates": null, "builder_name": "big_patent", "config_name": "y", "version": "2.1.2", "splits": {"train": {"name": "train", "num_bytes": 3695589005, "num_examples": 124397, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 200369780, "num_examples": 6911, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 204394948, "num_examples": 6911, "dataset_name": "big_patent"}}, "download_checksums": {"data/2.1.2/train.zip": {"num_bytes": 9129045386, "checksum": "59cae1439b01102faa5b9e1bb3061867ba292a6093cf66ee3d1bc33030d7a91f"}, "data/2.1.2/val.zip": {"num_bytes": 506259325, "checksum": "9206de008607095bdb29f405ef9b525565556860d015cbd4261154644b51ce9b"}, "data/2.1.2/test.zip": {"num_bytes": 507619065, "checksum": "614821d8d8c92451ba495e0b381f157a0e0b36977a70f2d4cb3ae4b6b706539a"}}, "download_size": 10142923776, "post_processing_size": null, "dataset_size": 4100353733, "size_in_bytes": 14243277509}}