update

copa.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:261a8413a7fa5af6b129dee1c435976f4c7086187c5ea3dd5fd14939ee14bb92
+size 40851

notebooks/.ipynb_checkpoints/convert-lm-eval-harness-checkpoint.ipynb

@@ -2,69 +2,46 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "c975c670-97cc-453e-bba6-3639cf8d5e89",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "from datasets import load_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e818dca8-bd10-4b89-9fcd-5cd9252b4e07",
-   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Found cached dataset hellaswag (/home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae)\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 461.50it/s]\n",
-      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-aec62727e66b6615.arrow\n",
-      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-e645cff193ea8a1d.arrow\n",
-      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-70b4a183b087a019.arrow\n"
+      "/home/jue@together.xyz/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     }
    ],
    "source": [
-    "task_name = 'hellaswag'\n",
-    "data = load_dataset(task_name)\n",
-    "data.shuffle(seed=42)\n",
-    "with open(f'../{task_name}.jsonl', 'w') as f:\n",
-    "    for i_item, item in enumerate(data['train']):\n",
-    "        text = item['ctx'] + item['endings'][int(item['label'])]\n",
-    "        f.write(\n",
-    "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
-    "        )"
+    "import json\n",
+    "from datasets import load_dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "c42aae00-be85-4b64-9325-f6a6139c6ee6",
+   "execution_count": 2,
+   "id": "e818dca8-bd10-4b89-9fcd-5cd9252b4e07",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Found cached dataset boolq (/home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5)\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 845.37it/s]\n",
-      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5/cache-b77c77fd8280863e.arrow\n",
-      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5/cache-a6a68560e7f35615.arrow\n"
+      "Found cached dataset hellaswag (/home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae)\n",
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 53.00it/s]\n"
      ]
     }
    ],
    "source": [
-    "task_name = 'boolq'\n",
+    "task_name = 'hellaswag'\n",
     "data = load_dataset(task_name)\n",
     "data.shuffle(seed=42)\n",
     "with open(f'../{task_name}.jsonl', 'w') as f:\n",
     "    for i_item, item in enumerate(data['train']):\n",
-    "        text = f\"{item['passage']}\\nQuestion: {item['question']}?\\nAnswer: {item['answer']}\"\n",
+    "        text = item['ctx'] + item['endings'][int(item['label'])]\n",
     "        f.write(\n",
     "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
     "        )"
@@ -72,31 +49,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "878216f4-74e4-46ba-bfcd-c95348c10415",
+   "execution_count": 3,
+   "id": "c42aae00-be85-4b64-9325-f6a6139c6ee6",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Downloading builder script: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.37k/5.37k [00:00<00:00, 38.7MB/s]\n",
-      "Downloading metadata: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.47k/4.47k [00:00<00:00, 32.7MB/s]\n",
-      "Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8.66k/8.66k [00:00<00:00, 53.6MB/s]\n"
+      "Downloading builder script: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.38k/3.38k [00:00<00:00, 32.1MB/s]\n",
+      "Downloading metadata: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.91k/1.91k [00:00<00:00, 20.0MB/s]\n",
+      "Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6.60k/6.60k [00:00<00:00, 45.1MB/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading and preparing dataset ai2_arc/ARC-Challenge to /home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Challenge/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6...\n"
+      "Downloading and preparing dataset boolq/default to /home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5...\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 681M/681M [00:17<00:00, 38.8MB/s]\n",
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+      "Downloading data:  28%|███████████████████████████████████████▉                                                                                                         | 617k/2.24M [00:00<00:01, 1.49MB/s]\u001b[A\n",
+      "Downloading data:  49%|██████████████████████████████████████████████████████████████████████▏                                                                         | 1.09M/2.24M [00:00<00:00, 2.53MB/s]\u001b[A\n",
+      "Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.24M/2.24M [00:00<00:00, 2.61MB/s]\u001b[A\n",
+      "Downloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:03<00:00,  1.85s/it]\n",
       "                                                                                                                                                                                                            \r"
      ]
     },
@@ -104,14 +102,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dataset ai2_arc downloaded and prepared to /home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Challenge/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6. Subsequent calls will reuse this data.\n"
+      "Dataset boolq downloaded and prepared to /home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5. Subsequent calls will reuse this data.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 509.10it/s]\n"
+      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 139.08it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "task_name = 'boolq'\n",
+    "data = load_dataset(task_name)\n",
+    "data.shuffle(seed=42)\n",
+    "with open(f'../{task_name}.jsonl', 'w') as f:\n",
+    "    for i_item, item in enumerate(data['train']):\n",
+    "        text = f\"{item['passage']}\\nQuestion: {item['question']}?\\nAnswer: {item['answer']}\"\n",
+    "        f.write(\n",
+    "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "878216f4-74e4-46ba-bfcd-c95348c10415",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset ai2_arc (/home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Challenge/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6)\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 624.09it/s]\n"
      ]
     }
    ],
@@ -132,49 +157,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "id": "7cb51285-d5ce-4ae2-bd7f-ac15e87c4fb7",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Downloading and preparing dataset ai2_arc/ARC-Easy to /home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Easy/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6...\n"
+      "Found cached dataset ai2_arc (/home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Easy/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6)\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 426.87it/s]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "task_name = 'arc_easy'\n",
+    "data = load_dataset('ai2_arc', 'ARC-Easy')\n",
+    "data.shuffle(seed=42)\n",
+    "with open(f'../{task_name}.jsonl', 'w') as f:\n",
+    "    for i_item, item in enumerate(data['train']):\n",
+    "        i_a = item['choices']['label'].index(item['answerKey'])\n",
+    "        q = item['question']\n",
+    "        a = item['choices']['text'][i_a]\n",
+    "        text = \"Question: \" + q + \"\\nAnswer:\" + a\n",
+    "        f.write(\n",
+    "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9347c339-4559-4111-bf44-ce2b624edc9e",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "                                                                                                                                                                                                            \r"
+      "Found cached dataset super_glue (/home/jue@together.xyz/.cache/huggingface/datasets/super_glue/copa/1.0.3/bb9675f958ebfee0d5d6dc5476fafe38c79123727a7258d515c450873dbdbbed)\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1055.52it/s]\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/super_glue/copa/1.0.3/bb9675f958ebfee0d5d6dc5476fafe38c79123727a7258d515c450873dbdbbed/cache-06e156489a3d17ef.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/super_glue/copa/1.0.3/bb9675f958ebfee0d5d6dc5476fafe38c79123727a7258d515c450873dbdbbed/cache-f4bd523109c343ed.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/super_glue/copa/1.0.3/bb9675f958ebfee0d5d6dc5476fafe38c79123727a7258d515c450873dbdbbed/cache-b8d0981688b46f46.arrow\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "task_name = 'copa'\n",
+    "data = load_dataset('super_glue', task_name)\n",
+    "data.shuffle(seed=42)\n",
+    "with open(f'../{task_name}.jsonl', 'w') as f:\n",
+    "    for i_item, item in enumerate(data['train']):\n",
+    "        i_a = item['label']\n",
+    "        premise = item['premise']\n",
+    "        question = item['question']\n",
+    "        choice = item['choice1'] if i_a == 0 else item['choice2']\n",
+    "        choice = choice[0].lower() + choice[1:]\n",
+    "\n",
+    "        connector = {\n",
+    "            \"cause\": \"because\",\n",
+    "            \"effect\": \"therefore\",\n",
+    "        }[question]\n",
+    "\n",
+    "        text = premise.strip()[:-1] + f\" {connector}\" + ' ' + choice\n",
+    "        f.write(\n",
+    "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dc24ba3b-aa6a-4665-87aa-08471a87de9b",
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Dataset ai2_arc downloaded and prepared to /home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Easy/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6. Subsequent calls will reuse this data.\n"
+      "Found cached dataset piqa (/home/jue@together.xyz/.cache/huggingface/datasets/piqa/plain_text/1.1.0/6c611c1a9bf220943c4174e117d3b660859665baf1d43156230116185312d011)\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████���█████████████████████████████████████████████████████| 3/3 [00:00<00:00, 910.95it/s]\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/piqa/plain_text/1.1.0/6c611c1a9bf220943c4174e117d3b660859665baf1d43156230116185312d011/cache-64434cd628e6e9e0.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/piqa/plain_text/1.1.0/6c611c1a9bf220943c4174e117d3b660859665baf1d43156230116185312d011/cache-97a6e3c466ced62d.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/piqa/plain_text/1.1.0/6c611c1a9bf220943c4174e117d3b660859665baf1d43156230116185312d011/cache-6b1e5924150382c7.arrow\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "task_name = 'piqa'\n",
+    "data = load_dataset(task_name)\n",
+    "data.shuffle(seed=42)\n",
+    "with open(f'../{task_name}.jsonl', 'w') as f:\n",
+    "    for i_item, item in enumerate(data['train']):\n",
+    "        i_a = item['label']\n",
+    "        goal = item['goal']\n",
+    "        sol = item['sol1'] if i_a == 0 else item['sol2']\n",
+    "\n",
+    "        text = \"Question: \" + goal + \"\\nAnswer: \" + sol\n",
+    "        f.write(\n",
+    "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "501744bd-0260-4a3c-89dc-df264ff3306e",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 439.03it/s]\n"
+      "Found cached dataset winogrande (/home/jue@together.xyz/.cache/huggingface/datasets/winogrande/winogrande_xl/1.1.0/a826c3d3506aefe0e9e9390dcb53271070536586bab95849876b2c1743df56e2)\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1014.75it/s]\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/winogrande/winogrande_xl/1.1.0/a826c3d3506aefe0e9e9390dcb53271070536586bab95849876b2c1743df56e2/cache-ebcf1ebbadaf243c.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/winogrande/winogrande_xl/1.1.0/a826c3d3506aefe0e9e9390dcb53271070536586bab95849876b2c1743df56e2/cache-ea79c2b85e9d846f.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/winogrande/winogrande_xl/1.1.0/a826c3d3506aefe0e9e9390dcb53271070536586bab95849876b2c1743df56e2/cache-5aca6c830a1dfa33.arrow\n"
      ]
     }
    ],
    "source": [
-    "task_name = 'arc_easy'\n",
-    "data = load_dataset('ai2_arc', 'ARC-Easy')\n",
+    "task_name = 'winogrande'\n",
+    "data = load_dataset(task_name, 'winogrande_xl')\n",
     "data.shuffle(seed=42)\n",
     "with open(f'../{task_name}.jsonl', 'w') as f:\n",
     "    for i_item, item in enumerate(data['train']):\n",
-    "        i_a = item['choices']['label'].index(item['answerKey'])\n",
-    "        q = item['question']\n",
-    "        a = item['choices']['text'][i_a]\n",
-    "        text = \"Question: \" + q + \"\\nAnswer:\" + a\n",
+    "        i_a = item['answer']\n",
+    "        sentence = item['sentence']\n",
+    "        option = item['option1'] if i_a == 1 else item['option2']\n",
+    "        \n",
+    "        text = sentence.replace('_', option)\n",
     "        f.write(\n",
     "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
     "        )"
@@ -182,42 +296,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "b3b98d73-4729-40a1-a5ea-51a3bcfd7ffe",
+   "execution_count": 9,
+   "id": "65d4b641-2b78-476f-89ac-6ecb48e7d044",
    "metadata": {},
    "outputs": [
     {
-     "ename": "FileNotFoundError",
-     "evalue": "Couldn't find a dataset script at /var/cr06_data/jue@together.xyz/target-data/notebooks/juewang/target-data/target-data.py or any data file in the same directory. Couldn't find 'juewang/target-data' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['test[-._ 0-9/]**', '**[-._ 0-9/]test[-._ 0-9/]**', 'testing[-._ 0-9/]**', '**[-._ 0-9/]testing[-._ 0-9/]**', 'eval[-._ 0-9/]**', '**[-._ 0-9/]eval[-._ 0-9/]**', 'evaluation[-._ 0-9/]**', '**[-._ 0-9/]evaluation[-._ 0-9/]**'] in dataset repository juewang/target-data with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mjuewang/target-data\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msplit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/load.py:1785\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m   1780\u001b[0m verification_mode \u001b[38;5;241m=\u001b[39m VerificationMode(\n\u001b[1;32m   1781\u001b[0m     (verification_mode \u001b[38;5;129;01mor\u001b[39;00m VerificationMode\u001b[38;5;241m.\u001b[39mBASIC_CHECKS) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m save_infos \u001b[38;5;28;01melse\u001b[39;00m VerificationMode\u001b[38;5;241m.\u001b[39mALL_CHECKS\n\u001b[1;32m   1782\u001b[0m )\n\u001b[1;32m   1784\u001b[0m \u001b[38;5;66;03m# Create a dataset builder\u001b[39;00m\n\u001b[0;32m-> 1785\u001b[0m builder_instance \u001b[38;5;241m=\u001b[39m \u001b[43mload_dataset_builder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1786\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1787\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1788\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1789\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_files\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1790\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1791\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1792\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1793\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1795\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_auth_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1796\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1797\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mconfig_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1798\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1800\u001b[0m \u001b[38;5;66;03m# Return iterable dataset in case of streaming\u001b[39;00m\n\u001b[1;32m   1801\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m streaming:\n",
-      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/load.py:1514\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, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m   1512\u001b[0m     download_config \u001b[38;5;241m=\u001b[39m download_config\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m download_config \u001b[38;5;28;01melse\u001b[39;00m DownloadConfig()\n\u001b[1;32m   1513\u001b[0m     download_config\u001b[38;5;241m.\u001b[39muse_auth_token \u001b[38;5;241m=\u001b[39m use_auth_token\n\u001b[0;32m-> 1514\u001b[0m dataset_module \u001b[38;5;241m=\u001b[39m \u001b[43mdataset_module_factory\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1515\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1516\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1517\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1518\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1519\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1520\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_files\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1521\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# Get dataset builder class from the processing script\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m builder_cls \u001b[38;5;241m=\u001b[39m import_main_class(dataset_module\u001b[38;5;241m.\u001b[39mmodule_path)\n",
-      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/load.py:1227\u001b[0m, in \u001b[0;36mdataset_module_factory\u001b[0;34m(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)\u001b[0m\n\u001b[1;32m   1225\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m e1 \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1226\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e1, \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m):\n\u001b[0;32m-> 1227\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m   1228\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find a dataset script at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrelative_to_absolute_path(combined_path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or any data file in the same directory. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1229\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m on the Hugging Face Hub either: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(e1)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me1\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1230\u001b[0m                 ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1231\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m e1 \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1232\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: Couldn't find a dataset script at /var/cr06_data/jue@together.xyz/target-data/notebooks/juewang/target-data/target-data.py or any data file in the same directory. Couldn't find 'juewang/target-data' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['test[-._ 0-9/]**', '**[-._ 0-9/]test[-._ 0-9/]**', 'testing[-._ 0-9/]**', '**[-._ 0-9/]testing[-._ 0-9/]**', 'eval[-._ 0-9/]**', '**[-._ 0-9/]eval[-._ 0-9/]**', 'evaluation[-._ 0-9/]**', '**[-._ 0-9/]evaluation[-._ 0-9/]**'] in dataset repository juewang/target-data with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset json (/home/jue@together.xyz/.cache/huggingface/datasets/juewang___json/juewang--target-data-72e09ed206c54e99/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96)\n"
      ]
     }
    ],
    "source": [
-    "data = load_dataset('juewang/target-data', split='train')"
+    "dataset = load_dataset(\"juewang/target-data\", data_files='*.jsonl', split='train')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "65d4b641-2b78-476f-89ac-6ecb48e7d044",
+   "id": "a0554b4f-c5c0-4621-8b91-3daf05e6fbdf",
    "metadata": {},
    "outputs": [],
    "source": []
-  },
-  {
-   "cell_type": "raw",
-   "id": "1e0b7ea2-5112-4118-b4ab-f0df5ef2bdc6",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {

notebooks/convert-lm-eval-harness.ipynb

@@ -2,69 +2,46 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "c975c670-97cc-453e-bba6-3639cf8d5e89",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "from datasets import load_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e818dca8-bd10-4b89-9fcd-5cd9252b4e07",
-   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Found cached dataset hellaswag (/home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae)\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 461.50it/s]\n",
-      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-aec62727e66b6615.arrow\n",
-      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-e645cff193ea8a1d.arrow\n",
-      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-70b4a183b087a019.arrow\n"
+      "/home/jue@together.xyz/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     }
    ],
    "source": [
-    "task_name = 'hellaswag'\n",
-    "data = load_dataset(task_name)\n",
-    "data.shuffle(seed=42)\n",
-    "with open(f'../{task_name}.jsonl', 'w') as f:\n",
-    "    for i_item, item in enumerate(data['train']):\n",
-    "        text = item['ctx'] + item['endings'][int(item['label'])]\n",
-    "        f.write(\n",
-    "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
-    "        )"
+    "import json\n",
+    "from datasets import load_dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "c42aae00-be85-4b64-9325-f6a6139c6ee6",
+   "execution_count": 2,
+   "id": "e818dca8-bd10-4b89-9fcd-5cd9252b4e07",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Found cached dataset boolq (/home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5)\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 845.37it/s]\n",
-      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5/cache-b77c77fd8280863e.arrow\n",
-      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5/cache-a6a68560e7f35615.arrow\n"
+      "Found cached dataset hellaswag (/home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae)\n",
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 53.00it/s]\n"
      ]
     }
    ],
    "source": [
-    "task_name = 'boolq'\n",
+    "task_name = 'hellaswag'\n",
     "data = load_dataset(task_name)\n",
     "data.shuffle(seed=42)\n",
     "with open(f'../{task_name}.jsonl', 'w') as f:\n",
     "    for i_item, item in enumerate(data['train']):\n",
-    "        text = f\"{item['passage']}\\nQuestion: {item['question']}?\\nAnswer: {item['answer']}\"\n",
+    "        text = item['ctx'] + item['endings'][int(item['label'])]\n",
     "        f.write(\n",
     "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
     "        )"
@@ -72,31 +49,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "878216f4-74e4-46ba-bfcd-c95348c10415",
+   "execution_count": 3,
+   "id": "c42aae00-be85-4b64-9325-f6a6139c6ee6",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Downloading builder script: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.37k/5.37k [00:00<00:00, 38.7MB/s]\n",
-      "Downloading metadata: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.47k/4.47k [00:00<00:00, 32.7MB/s]\n",
-      "Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8.66k/8.66k [00:00<00:00, 53.6MB/s]\n"
+      "Downloading builder script: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.38k/3.38k [00:00<00:00, 32.1MB/s]\n",
+      "Downloading metadata: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.91k/1.91k [00:00<00:00, 20.0MB/s]\n",
+      "Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6.60k/6.60k [00:00<00:00, 45.1MB/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading and preparing dataset ai2_arc/ARC-Challenge to /home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Challenge/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6...\n"
+      "Downloading and preparing dataset boolq/default to /home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5...\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 681M/681M [00:17<00:00, 38.8MB/s]\n",
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+      "Downloading data:  31%|█████████████████████████████████████████████▏                                                                                                  | 2.05M/6.53M [00:00<00:00, 4.67MB/s]\u001b[A\n",
+      "Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6.53M/6.53M [00:01<00:00, 6.28MB/s]\u001b[A\n",
+      "Downloading data files:  50%|████████████████████████████████████████████████████████████████████████▌                                                                        | 1/2 [00:01<00:01,  1.95s/it]\n",
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+      "Downloading data:   5%|██████▊                                                                                                                                           | 104k/2.24M [00:00<00:06, 347kB/s]\u001b[A\n",
+      "Downloading data:   9%|████████████▋                                                                                                                                     | 195k/2.24M [00:00<00:03, 549kB/s]\u001b[A\n",
+      "Downloading data:  15%|██████████████████████▏                                                                                                                           | 341k/2.24M [00:00<00:02, 863kB/s]\u001b[A\n",
+      "Downloading data:  28%|███████████████████████████████████████▉                                                                                                         | 617k/2.24M [00:00<00:01, 1.49MB/s]\u001b[A\n",
+      "Downloading data:  49%|██████████████████████████████████████████████████████████████████████▏                                                                         | 1.09M/2.24M [00:00<00:00, 2.53MB/s]\u001b[A\n",
+      "Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.24M/2.24M [00:00<00:00, 2.61MB/s]\u001b[A\n",
+      "Downloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:03<00:00,  1.85s/it]\n",
       "                                                                                                                                                                                                            \r"
      ]
     },
@@ -104,14 +102,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dataset ai2_arc downloaded and prepared to /home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Challenge/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6. Subsequent calls will reuse this data.\n"
+      "Dataset boolq downloaded and prepared to /home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5. Subsequent calls will reuse this data.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 509.10it/s]\n"
+      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 139.08it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "task_name = 'boolq'\n",
+    "data = load_dataset(task_name)\n",
+    "data.shuffle(seed=42)\n",
+    "with open(f'../{task_name}.jsonl', 'w') as f:\n",
+    "    for i_item, item in enumerate(data['train']):\n",
+    "        text = f\"{item['passage']}\\nQuestion: {item['question']}?\\nAnswer: {item['answer']}\"\n",
+    "        f.write(\n",
+    "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "878216f4-74e4-46ba-bfcd-c95348c10415",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset ai2_arc (/home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Challenge/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6)\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 624.09it/s]\n"
      ]
     }
    ],
@@ -132,49 +157,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "id": "7cb51285-d5ce-4ae2-bd7f-ac15e87c4fb7",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Downloading and preparing dataset ai2_arc/ARC-Easy to /home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Easy/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6...\n"
+      "Found cached dataset ai2_arc (/home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Easy/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6)\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 426.87it/s]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "task_name = 'arc_easy'\n",
+    "data = load_dataset('ai2_arc', 'ARC-Easy')\n",
+    "data.shuffle(seed=42)\n",
+    "with open(f'../{task_name}.jsonl', 'w') as f:\n",
+    "    for i_item, item in enumerate(data['train']):\n",
+    "        i_a = item['choices']['label'].index(item['answerKey'])\n",
+    "        q = item['question']\n",
+    "        a = item['choices']['text'][i_a]\n",
+    "        text = \"Question: \" + q + \"\\nAnswer:\" + a\n",
+    "        f.write(\n",
+    "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9347c339-4559-4111-bf44-ce2b624edc9e",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "                                                                                                                                                                                                            \r"
+      "Found cached dataset super_glue (/home/jue@together.xyz/.cache/huggingface/datasets/super_glue/copa/1.0.3/bb9675f958ebfee0d5d6dc5476fafe38c79123727a7258d515c450873dbdbbed)\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1055.52it/s]\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/super_glue/copa/1.0.3/bb9675f958ebfee0d5d6dc5476fafe38c79123727a7258d515c450873dbdbbed/cache-06e156489a3d17ef.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/super_glue/copa/1.0.3/bb9675f958ebfee0d5d6dc5476fafe38c79123727a7258d515c450873dbdbbed/cache-f4bd523109c343ed.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/super_glue/copa/1.0.3/bb9675f958ebfee0d5d6dc5476fafe38c79123727a7258d515c450873dbdbbed/cache-b8d0981688b46f46.arrow\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "task_name = 'copa'\n",
+    "data = load_dataset('super_glue', task_name)\n",
+    "data.shuffle(seed=42)\n",
+    "with open(f'../{task_name}.jsonl', 'w') as f:\n",
+    "    for i_item, item in enumerate(data['train']):\n",
+    "        i_a = item['label']\n",
+    "        premise = item['premise']\n",
+    "        question = item['question']\n",
+    "        choice = item['choice1'] if i_a == 0 else item['choice2']\n",
+    "        choice = choice[0].lower() + choice[1:]\n",
+    "\n",
+    "        connector = {\n",
+    "            \"cause\": \"because\",\n",
+    "            \"effect\": \"therefore\",\n",
+    "        }[question]\n",
+    "\n",
+    "        text = premise.strip()[:-1] + f\" {connector}\" + ' ' + choice\n",
+    "        f.write(\n",
+    "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dc24ba3b-aa6a-4665-87aa-08471a87de9b",
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Dataset ai2_arc downloaded and prepared to /home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Easy/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6. Subsequent calls will reuse this data.\n"
+      "Found cached dataset piqa (/home/jue@together.xyz/.cache/huggingface/datasets/piqa/plain_text/1.1.0/6c611c1a9bf220943c4174e117d3b660859665baf1d43156230116185312d011)\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████���█████████████████████████████████████████████████████| 3/3 [00:00<00:00, 910.95it/s]\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/piqa/plain_text/1.1.0/6c611c1a9bf220943c4174e117d3b660859665baf1d43156230116185312d011/cache-64434cd628e6e9e0.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/piqa/plain_text/1.1.0/6c611c1a9bf220943c4174e117d3b660859665baf1d43156230116185312d011/cache-97a6e3c466ced62d.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/piqa/plain_text/1.1.0/6c611c1a9bf220943c4174e117d3b660859665baf1d43156230116185312d011/cache-6b1e5924150382c7.arrow\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "task_name = 'piqa'\n",
+    "data = load_dataset(task_name)\n",
+    "data.shuffle(seed=42)\n",
+    "with open(f'../{task_name}.jsonl', 'w') as f:\n",
+    "    for i_item, item in enumerate(data['train']):\n",
+    "        i_a = item['label']\n",
+    "        goal = item['goal']\n",
+    "        sol = item['sol1'] if i_a == 0 else item['sol2']\n",
+    "\n",
+    "        text = \"Question: \" + goal + \"\\nAnswer: \" + sol\n",
+    "        f.write(\n",
+    "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "501744bd-0260-4a3c-89dc-df264ff3306e",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 439.03it/s]\n"
+      "Found cached dataset winogrande (/home/jue@together.xyz/.cache/huggingface/datasets/winogrande/winogrande_xl/1.1.0/a826c3d3506aefe0e9e9390dcb53271070536586bab95849876b2c1743df56e2)\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1014.75it/s]\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/winogrande/winogrande_xl/1.1.0/a826c3d3506aefe0e9e9390dcb53271070536586bab95849876b2c1743df56e2/cache-ebcf1ebbadaf243c.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/winogrande/winogrande_xl/1.1.0/a826c3d3506aefe0e9e9390dcb53271070536586bab95849876b2c1743df56e2/cache-ea79c2b85e9d846f.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/winogrande/winogrande_xl/1.1.0/a826c3d3506aefe0e9e9390dcb53271070536586bab95849876b2c1743df56e2/cache-5aca6c830a1dfa33.arrow\n"
      ]
     }
    ],
    "source": [
-    "task_name = 'arc_easy'\n",
-    "data = load_dataset('ai2_arc', 'ARC-Easy')\n",
+    "task_name = 'winogrande'\n",
+    "data = load_dataset(task_name, 'winogrande_xl')\n",
     "data.shuffle(seed=42)\n",
     "with open(f'../{task_name}.jsonl', 'w') as f:\n",
     "    for i_item, item in enumerate(data['train']):\n",
-    "        i_a = item['choices']['label'].index(item['answerKey'])\n",
-    "        q = item['question']\n",
-    "        a = item['choices']['text'][i_a]\n",
-    "        text = \"Question: \" + q + \"\\nAnswer:\" + a\n",
+    "        i_a = item['answer']\n",
+    "        sentence = item['sentence']\n",
+    "        option = item['option1'] if i_a == 1 else item['option2']\n",
+    "        \n",
+    "        text = sentence.replace('_', option)\n",
     "        f.write(\n",
     "            json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
     "        )"
@@ -182,42 +296,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "b3b98d73-4729-40a1-a5ea-51a3bcfd7ffe",
+   "execution_count": 9,
+   "id": "65d4b641-2b78-476f-89ac-6ecb48e7d044",
    "metadata": {},
    "outputs": [
     {
-     "ename": "FileNotFoundError",
-     "evalue": "Couldn't find a dataset script at /var/cr06_data/jue@together.xyz/target-data/notebooks/juewang/target-data/target-data.py or any data file in the same directory. Couldn't find 'juewang/target-data' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['test[-._ 0-9/]**', '**[-._ 0-9/]test[-._ 0-9/]**', 'testing[-._ 0-9/]**', '**[-._ 0-9/]testing[-._ 0-9/]**', 'eval[-._ 0-9/]**', '**[-._ 0-9/]eval[-._ 0-9/]**', 'evaluation[-._ 0-9/]**', '**[-._ 0-9/]evaluation[-._ 0-9/]**'] in dataset repository juewang/target-data with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mjuewang/target-data\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msplit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/load.py:1785\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m   1780\u001b[0m verification_mode \u001b[38;5;241m=\u001b[39m VerificationMode(\n\u001b[1;32m   1781\u001b[0m     (verification_mode \u001b[38;5;129;01mor\u001b[39;00m VerificationMode\u001b[38;5;241m.\u001b[39mBASIC_CHECKS) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m save_infos \u001b[38;5;28;01melse\u001b[39;00m VerificationMode\u001b[38;5;241m.\u001b[39mALL_CHECKS\n\u001b[1;32m   1782\u001b[0m )\n\u001b[1;32m   1784\u001b[0m \u001b[38;5;66;03m# Create a dataset builder\u001b[39;00m\n\u001b[0;32m-> 1785\u001b[0m builder_instance \u001b[38;5;241m=\u001b[39m \u001b[43mload_dataset_builder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1786\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1787\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1788\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1789\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_files\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1790\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1791\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1792\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1793\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1795\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_auth_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1796\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1797\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mconfig_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1798\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1800\u001b[0m \u001b[38;5;66;03m# Return iterable dataset in case of streaming\u001b[39;00m\n\u001b[1;32m   1801\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m streaming:\n",
-      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/load.py:1514\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, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m   1512\u001b[0m     download_config \u001b[38;5;241m=\u001b[39m download_config\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m download_config \u001b[38;5;28;01melse\u001b[39;00m DownloadConfig()\n\u001b[1;32m   1513\u001b[0m     download_config\u001b[38;5;241m.\u001b[39muse_auth_token \u001b[38;5;241m=\u001b[39m use_auth_token\n\u001b[0;32m-> 1514\u001b[0m dataset_module \u001b[38;5;241m=\u001b[39m \u001b[43mdataset_module_factory\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1515\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1516\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1517\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1518\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1519\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1520\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_files\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1521\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# Get dataset builder class from the processing script\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m builder_cls \u001b[38;5;241m=\u001b[39m import_main_class(dataset_module\u001b[38;5;241m.\u001b[39mmodule_path)\n",
-      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/load.py:1227\u001b[0m, in \u001b[0;36mdataset_module_factory\u001b[0;34m(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)\u001b[0m\n\u001b[1;32m   1225\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m e1 \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1226\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e1, \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m):\n\u001b[0;32m-> 1227\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m   1228\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find a dataset script at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrelative_to_absolute_path(combined_path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or any data file in the same directory. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1229\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m on the Hugging Face Hub either: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(e1)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me1\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1230\u001b[0m                 ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1231\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m e1 \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1232\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: Couldn't find a dataset script at /var/cr06_data/jue@together.xyz/target-data/notebooks/juewang/target-data/target-data.py or any data file in the same directory. Couldn't find 'juewang/target-data' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['test[-._ 0-9/]**', '**[-._ 0-9/]test[-._ 0-9/]**', 'testing[-._ 0-9/]**', '**[-._ 0-9/]testing[-._ 0-9/]**', 'eval[-._ 0-9/]**', '**[-._ 0-9/]eval[-._ 0-9/]**', 'evaluation[-._ 0-9/]**', '**[-._ 0-9/]evaluation[-._ 0-9/]**'] in dataset repository juewang/target-data with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset json (/home/jue@together.xyz/.cache/huggingface/datasets/juewang___json/juewang--target-data-72e09ed206c54e99/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96)\n"
      ]
     }
    ],
    "source": [
-    "data = load_dataset('juewang/target-data', split='train')"
+    "dataset = load_dataset(\"juewang/target-data\", data_files='*.jsonl', split='train')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "65d4b641-2b78-476f-89ac-6ecb48e7d044",
+   "id": "a0554b4f-c5c0-4621-8b91-3daf05e6fbdf",
    "metadata": {},
    "outputs": [],
    "source": []
-  },
-  {
-   "cell_type": "raw",
-   "id": "1e0b7ea2-5112-4118-b4ab-f0df5ef2bdc6",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {

piqa.jsonl

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+oid sha256:68f23de850360818d0e3733f9fe1540929a9de22e53d25145d831c5932ad520b
+size 3003394

winogrande.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac8f07178644445f6421314b899ff976f49c25804d43cd163734cf139f380681
+size 5711173