Add cni data files

README.md

@@ -112,13 +112,13 @@ dataset_info:
           '2': contradiction
   splits:
   - name: validation
-    num_bytes: 113264
+    num_bytes: 113256
     num_examples: 658
   - name: test
-    num_bytes: 116292
+    num_bytes: 116284
     num_examples: 750
-  download_size: 2256093
-  dataset_size: 229556
+  download_size: 78899
+  dataset_size: 229540
 - config_name: gn
   features:
   - name: premise
@@ -286,6 +286,12 @@ configs:
     path: bzd/validation-*
   - split: test
     path: bzd/test-*
+- config_name: cni
+  data_files:
+  - split: validation
+    path: cni/validation-*
+  - split: test
+    path: cni/test-*
 ---
 
 # Dataset Card for AmericasNLI

cni/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab5948cebe3927db3e19464ea0aa3bf8a53c87ea9a730526ccb875fa009922c4
+size 40572

cni/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56630838e3349201cc9a7095da4144c1a974765c285aea09e4c6dc3e14001459
+size 38327

dataset_infos.json

@@ -104,35 +104,28 @@
     "cni": {
         "description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n",
         "citation": "\n@article{DBLP:journals/corr/abs-2104-08726,\n  author    = {Abteen Ebrahimi and\n               Manuel Mager and\n               Arturo Oncevay and\n               Vishrav Chaudhary and\n               Luis Chiruzzo and\n               Angela Fan and\n               John Ortega and\n               Ricardo Ramos and\n               Annette Rios and\n               Ivan Vladimir and\n               Gustavo A. Gim{'{e}}nez{-}Lugo and\n               Elisabeth Mager and\n               Graham Neubig and\n               Alexis Palmer and\n               Rolando A. Coto Solano and\n               Ngoc Thang Vu and\n               Katharina Kann},\n  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of\n               Pretrained Multilingual Models in Truly Low-resource Languages},\n  journal   = {CoRR},\n  volume    = {abs/2104.08726},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.08726},\n  eprinttype = {arXiv},\n  eprint    = {2104.08726},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n",
-        "homepage": "TODO",
+        "homepage": "https://github.com/nala-cub/AmericasNLI",
         "license": "",
         "features": {
             "premise": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "hypothesis": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "label": {
-                "num_classes": 3,
                 "names": [
                     "entailment",
                     "neutral",
                     "contradiction"
                 ],
-                "names_file": null,
-                "id": null,
                 "_type": "ClassLabel"
             }
         },
-        "post_processed": null,
-        "supervised_keys": null,
-        "task_templates": null,
         "builder_name": "americas_nli",
+        "dataset_name": "americas_nli",
         "config_name": "cni",
         "version": {
             "version_str": "1.0.0",
@@ -144,31 +137,20 @@
         "splits": {
             "validation": {
                 "name": "validation",
-                "num_bytes": 113264,
+                "num_bytes": 113256,
                 "num_examples": 658,
-                "dataset_name": "americas_nli"
+                "dataset_name": null
             },
             "test": {
                 "name": "test",
-                "num_bytes": 116292,
+                "num_bytes": 116284,
                 "num_examples": 750,
-                "dataset_name": "americas_nli"
-            }
-        },
-        "download_checksums": {
-            "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv": {
-                "num_bytes": 1090405,
-                "checksum": "a2678f2820a2008547c5d993118979cc82a25d51a73570571566a1b74d8e8530"
-            },
-            "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv": {
-                "num_bytes": 1165688,
-                "checksum": "1e16c058de33ddaab4a037b1078a31ecab08afddfdc840140593b6da1439bcf8"
+                "dataset_name": null
             }
         },
-        "download_size": 2256093,
-        "post_processing_size": null,
-        "dataset_size": 229556,
-        "size_in_bytes": 2485649
+        "download_size": 78899,
+        "dataset_size": 229540,
+        "size_in_bytes": 308439
     },
     "gn": {
         "description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n",