Convert dataset to Parquet

Convert dataset to Parquet.

README.md

@@ -19,6 +19,7 @@ task_ids:
 - parsing
 pretty_name: AMTTL
 dataset_info:
+  config_name: amttl
   features:
   - name: id
     dtype: string
@@ -32,19 +33,28 @@ dataset_info:
           '1': I
           '2': E
           '3': S
-  config_name: amttl
   splits:
   - name: train
-    num_bytes: 1132212
+    num_bytes: 1132196
     num_examples: 3063
   - name: validation
-    num_bytes: 324374
+    num_bytes: 324358
     num_examples: 822
   - name: test
-    num_bytes: 328525
+    num_bytes: 328509
     num_examples: 908
-  download_size: 685534
-  dataset_size: 1785111
+  download_size: 274351
+  dataset_size: 1785063
+configs:
+- config_name: amttl
+  data_files:
+  - split: train
+    path: amttl/train-*
+  - split: validation
+    path: amttl/validation-*
+  - split: test
+    path: amttl/test-*
+  default: true
 ---
 
 # Dataset Card for AMTTL

amttl/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e162219c3d2e9a4b234407072169e58475c70f69a1118c4c92c1cc8bdb7fddcf
+size 51311

amttl/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93ff0e728fa5bf6cf4c32805ac01529c1b022f29b39f28406a5e7fd28b9b6342
+size 172615

amttl/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e7992b50bd6d87521937260ed7ebce5a986b8eb52ad0905373fe94d6b155c53e
+size 50425

dataset_infos.json

@@ -1 +1,65 @@
-{"amttl": {"description": "Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop\nwhen dealing with domain text, especially for a domain with lots of special terms and diverse\nwriting styles, such as the biomedical domain. However, building domain-specific CWS requires\nextremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant\nknowledge from high resource to low resource domains. Extensive experiments show that our mode\nachieves consistently higher accuracy than the single-task CWS and other transfer learning\nbaselines, especially when there is a large disparity between source and target domains.\n\nThis dataset is the accompanied medical Chinese word segmentation (CWS) dataset.\nThe tags are in BIES scheme.\n\nFor more details see https://www.aclweb.org/anthology/C18-1307/\n", "citation": "@inproceedings{xing2018adaptive,\n  title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},\n  author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},\n  booktitle={Proceedings of the 27th International Conference on Computational Linguistics},\n  pages={3619--3630},\n  year={2018}\n}\n", "homepage": "https://www.aclweb.org/anthology/C18-1307/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "tags": {"feature": {"num_classes": 4, "names": ["B", "I", "E", "S"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "amttl", "config_name": "amttl", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1132212, "num_examples": 3063, "dataset_name": "amttl"}, "validation": {"name": "validation", "num_bytes": 324374, "num_examples": 822, "dataset_name": "amttl"}, "test": {"name": "test", "num_bytes": 328525, "num_examples": 908, "dataset_name": "amttl"}}, "download_checksums": {"https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/forum_train.txt": {"num_bytes": 434357, "checksum": "9819373963ea04d1d28844d5bc83b6b0332fad8b5f2e73092bcfc58dc6d6292a"}, "https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/forum_dev.txt": {"num_bytes": 124973, "checksum": "1a2eb461b98d2a9160baad7f76d003cc0917b998e8283bcffa52b71224dd9d17"}, "https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/forum_test.txt": {"num_bytes": 126204, "checksum": "aea1a8cf244cd565e94bd193a1eef7a10b16eeb0b6fbb6ed1d2fefbd55360dd6"}}, "download_size": 685534, "post_processing_size": null, "dataset_size": 1785111, "size_in_bytes": 2470645}}
+{
+    "amttl": {
+        "description": "Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop\nwhen dealing with domain text, especially for a domain with lots of special terms and diverse\nwriting styles, such as the biomedical domain. However, building domain-specific CWS requires\nextremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant\nknowledge from high resource to low resource domains. Extensive experiments show that our mode\nachieves consistently higher accuracy than the single-task CWS and other transfer learning\nbaselines, especially when there is a large disparity between source and target domains.\n\nThis dataset is the accompanied medical Chinese word segmentation (CWS) dataset.\nThe tags are in BIES scheme.\n\nFor more details see https://www.aclweb.org/anthology/C18-1307/\n",
+        "citation": "@inproceedings{xing2018adaptive,\n  title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},\n  author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},\n  booktitle={Proceedings of the 27th International Conference on Computational Linguistics},\n  pages={3619--3630},\n  year={2018}\n}\n",
+        "homepage": "https://www.aclweb.org/anthology/C18-1307/",
+        "license": "",
+        "features": {
+            "id": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "tokens": {
+                "feature": {
+                    "dtype": "string",
+                    "_type": "Value"
+                },
+                "_type": "Sequence"
+            },
+            "tags": {
+                "feature": {
+                    "names": [
+                        "B",
+                        "I",
+                        "E",
+                        "S"
+                    ],
+                    "_type": "ClassLabel"
+                },
+                "_type": "Sequence"
+            }
+        },
+        "builder_name": "parquet",
+        "dataset_name": "amttl",
+        "config_name": "amttl",
+        "version": {
+            "version_str": "1.0.0",
+            "major": 1,
+            "minor": 0,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 1132196,
+                "num_examples": 3063,
+                "dataset_name": null
+            },
+            "validation": {
+                "name": "validation",
+                "num_bytes": 324358,
+                "num_examples": 822,
+                "dataset_name": null
+            },
+            "test": {
+                "name": "test",
+                "num_bytes": 328509,
+                "num_examples": 908,
+                "dataset_name": null
+            }
+        },
+        "download_size": 274351,
+        "dataset_size": 1785063,
+        "size_in_bytes": 2059414
+    }
+}