First alpha of data and stuff

ctkfacts_nli.py

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+import os
+import pathlib
+from typing import overload
+import datasets
+import json
+
+from datasets.info import DatasetInfo
+
+_DESCRIPTION = """\
+CtkFactsNLI is a NLI version of the Czech CTKFacts dataset
+"""
+
+_CITATION = """\
+todo
+"""
+
+
+class CtkfactsNli(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "label": datasets.ClassLabel(names=["SUPPORTS", "REFUTES", "NOT ENOUGH INFO"]),
+                    # datasets.features.Sequence({"text": datasets.Value("string"),"answer_start": datasets.Value("int32"),})
+                    "evidence": datasets.Value("string"),
+                    "claim": datasets.Value("string"),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://fcheck.fel.cvut.cz/dataset/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_managers):
+        dir = pathlib.Path(__file__).parent.resolve()
+        return [
+            datasets.SplitGenerator(datasets.Split.TRAIN, {
+                "filepath": os.path.join(dir, "data", "train.jsonl")
+            }),
+            datasets.SplitGenerator(datasets.Split.VALIDATION, {
+                "filepath": os.path.join(dir, "data", "validation.jsonl")
+            }),
+            datasets.SplitGenerator(datasets.Split.TEST, {
+                "filepath": os.path.join(dir, "data", "test.jsonl")
+            }),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        key = 0
+        with open(filepath, encoding="utf-8") as f:
+            for line in f:
+                datapoint = json.loads(line)
+                yield key, {
+                    "id": datapoint["id"],
+                    "evidence": " ".join(datapoint["evidence"]),
+                    "claim": datapoint["claim"],
+                    "label": datapoint["label"]
+                }
+                key += 1

experiments.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 🤗 Experiments over hf dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datasets\n",
+    "from datasets import load_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using custom data configuration default\n"
+     ]
+    },
+    {
+     "ename": "NotImplementedError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/6z/tk1x_gtj40lgc1zd7y9pql3h0000gn/T/ipykernel_83405/3889717483.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ctkfacts_nli.py\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/opt/homebrew/lib/python3.9/site-packages/datasets/load.py\u001b[0m in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\u001b[0m\n\u001b[1;32m   1602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1603\u001b[0m     \u001b[0;31m# Create a dataset builder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1604\u001b[0;31m     builder_instance = load_dataset_builder(\n\u001b[0m\u001b[1;32m   1605\u001b[0m         \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1606\u001b[0m         \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/homebrew/lib/python3.9/site-packages/datasets/load.py\u001b[0m in \u001b[0;36mload_dataset_builder\u001b[0;34m(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\u001b[0m\n\u001b[1;32m   1460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1461\u001b[0m     \u001b[0;31m# Instantiate the dataset builder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1462\u001b[0;31m     builder_instance: DatasetBuilder = builder_cls(\n\u001b[0m\u001b[1;32m   1463\u001b[0m         \u001b[0mcache_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1464\u001b[0m         \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/homebrew/lib/python3.9/site-packages/datasets/builder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, writer_batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwriter_batch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1043\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGeneratorBasedBuilder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1044\u001b[0m         \u001b[0;31m# Batch size used by the ArrowWriter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1045\u001b[0m         \u001b[0;31m# It defines the number of samples that are kept in memory before writing them\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/homebrew/lib/python3.9/site-packages/datasets/builder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, cache_dir, name, hash, base_path, features, use_auth_token, namespace, data_files, data_dir, **config_kwargs)\u001b[0m\n\u001b[1;32m    265\u001b[0m         \u001b[0;31m# Prefill datasetinfo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m         \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_exported_dataset_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m         \u001b[0minfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    268\u001b[0m         \u001b[0minfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilder_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m         \u001b[0minfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/homebrew/lib/python3.9/site-packages/datasets/builder.py\u001b[0m in \u001b[0;36m_info\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    477\u001b[0m             \u001b[0minfo\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mDatasetInfo\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0minformation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m         \"\"\"\n\u001b[0;32m--> 479\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNotImplementedError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "load_dataset(\"ctkfacts_nli.py\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.7 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

test.py

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+import datasets
+
+datasets.load_dataset("ctkfacts_nli.py")