fix missing req. arg for new datasets package (#1334)

Co-authored-by: Li Jiang <[email protected]>

notebook/autogen_chatgpt_gpt4.ipynb

@@ -174,7 +174,7 @@
     "import datasets\n",
     "\n",
     "seed = 41\n",
-    "data = datasets.load_dataset(\"competition_math\")\n",
+    "data = datasets.load_dataset(\"competition_math\", trust_remote_code=True)\n",
     "train_data = data[\"train\"].shuffle(seed=seed)\n",
     "test_data = data[\"test\"].shuffle(seed=seed)\n",
     "n_tune_data = 20\n",
@@ -390,7 +390,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m[I 2023-08-01 22:38:01,549]\u001b[0m A new study created in memory with name: optuna\u001b[0m\n"
+      "\u001B[32m[I 2023-08-01 22:38:01,549]\u001B[0m A new study created in memory with name: optuna\u001B[0m\n"
      ]
     },
     {

notebook/autogen_openai_completion.ipynb

@@ -196,7 +196,7 @@
     "import datasets\n",
     "\n",
     "seed = 41\n",
-    "data = datasets.load_dataset(\"openai_humaneval\")[\"test\"].shuffle(seed=seed)\n",
+    "data = datasets.load_dataset(\"openai_humaneval\", trust_remote_code=True)[\"test\"].shuffle(seed=seed)\n",
     "n_tune_data = 20\n",
     "tune_data = [\n",
     "    {\n",
@@ -444,8 +444,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m[I 2023-07-30 04:19:08,150]\u001b[0m A new study created in memory with name: optuna\u001b[0m\n",
-      "\u001b[32m[I 2023-07-30 04:19:08,153]\u001b[0m A new study created in memory with name: optuna\u001b[0m\n"
+      "\u001B[32m[I 2023-07-30 04:19:08,150]\u001B[0m A new study created in memory with name: optuna\u001B[0m\n",
+      "\u001B[32m[I 2023-07-30 04:19:08,153]\u001B[0m A new study created in memory with name: optuna\u001B[0m\n"
      ]
     },
     {

notebook/research/autogen_code.ipynb

@@ -152,7 +152,7 @@
     "import datasets\n",
     "\n",
     "seed = 41\n",
-    "data = datasets.load_dataset(\"openai_humaneval\")[\"test\"].shuffle(seed=seed)\n",
+    "data = datasets.load_dataset(\"openai_humaneval\", trust_remote_code=True)[\"test\"].shuffle(seed=seed)\n",
     "data = data.select(range(len(data))).rename_column(\"prompt\", \"definition\").remove_columns([\"task_id\", \"canonical_solution\"])"
    ]
   },

notebook/research/math_level5counting.ipynb

@@ -121,7 +121,7 @@
     "import datasets\n",
     "\n",
     "seed = 41\n",
-    "data = datasets.load_dataset(\"competition_math\")\n",
+    "data = datasets.load_dataset(\"competition_math\", trust_remote_code=True)\n",
     "train_data = data[\"train\"].shuffle(seed=seed)\n",
     "test_data = data[\"test\"].shuffle(seed=seed)\n",
     "n_tune_data = 20\n",

notebook/tune_huggingface.ipynb

@@ -112,9 +112,7 @@
      ]
     }
    ],
-   "source": [
-    "raw_dataset = datasets.load_dataset(\"glue\", TASK)"
-   ]
+   "source": "raw_dataset = datasets.load_dataset(\"glue\", TASK, trust_remote_code=True)"
   },
   {
    "cell_type": "code",
@@ -425,9 +423,7 @@
    "execution_count": 14,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "metric = datasets.load_metric(\"glue\", TASK)"
-   ]
+   "source": "metric = datasets.load_metric(\"glue\", TASK, trust_remote_code=True)"
   },
   {
    "cell_type": "code",
@@ -646,15 +642,15 @@
     "def train_distilbert(config: dict):\n",
     "\n",
     "    # Load CoLA dataset and apply tokenizer\n",
-    "    cola_raw = datasets.load_dataset(\"glue\", TASK)\n",
+    "    cola_raw = datasets.load_dataset(\"glue\", TASK, trust_remote_code=True)\n",
     "    cola_encoded = cola_raw.map(tokenize, batched=True)\n",
     "    train_dataset, eval_dataset = cola_encoded[\"train\"], cola_encoded[\"validation\"]\n",
     "\n",
     "    model = AutoModelForSequenceClassification.from_pretrained(\n",
     "        MODEL_CHECKPOINT, num_labels=NUM_LABELS\n",
     "    )\n",
     "\n",
-    "    metric = datasets.load_metric(\"glue\", TASK)\n",
+    "    metric = datasets.load_metric(\"glue\", TASK, trust_remote_code=True)\n",
     "    def compute_metrics(eval_pred):\n",
     "        predictions, labels = eval_pred\n",
     "        predictions = np.argmax(predictions, axis=1)\n",
@@ -847,7 +843,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m Reusing dataset glue (/home/ec2-user/.cache/huggingface/datasets/glue/cola/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)\n",
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m Reusing dataset glue (/home/ec2-user/.cache/huggingface/datasets/glue/cola/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)\n",
       "  0%|          | 0/9 [00:00<?, ?ba/s]\n",
       " 22%|██▏       | 2/9 [00:00<00:00, 19.41ba/s]\n",
       " 56%|█████▌    | 5/9 [00:00<00:00, 20.98ba/s]\n",
@@ -856,25 +852,25 @@
       "100%|██████████| 2/2 [00:00<00:00, 42.79ba/s]\n",
       "  0%|          | 0/2 [00:00<?, ?ba/s]\n",
       "100%|██████████| 2/2 [00:00<00:00, 41.48ba/s]\n",
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_projector.bias']\n",
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m - This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m - This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', 'classifier.weight', 'classifier.bias']\n",
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_projector.bias']\n",
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m - This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m - This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', 'classifier.weight', 'classifier.bias']\n",
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m To disable this warning, you can either:\n",
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m \t- Avoid using `tokenizers` before the fork if possible\n",
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m \t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m To disable this warning, you can either:\n",
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m \t- Avoid using `tokenizers` before the fork if possible\n",
-      "\u001b[2m\u001b[36m(pid=11344)\u001b[0m \t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m To disable this warning, you can either:\n",
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m \t- Avoid using `tokenizers` before the fork if possible\n",
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m \t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m To disable this warning, you can either:\n",
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m \t- Avoid using `tokenizers` before the fork if possible\n",
+      "\u001B[2m\u001B[36m(pid=11344)\u001B[0m \t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
      ]
     }
    ],

test/autogen/oai/test_completion.py

@@ -187,7 +187,7 @@ def test_humaneval(num_samples=1):
     )
 
     seed = 41
-    data = datasets.load_dataset("openai_humaneval")["test"].shuffle(seed=seed)
+    data = datasets.load_dataset("openai_humaneval", trust_remote_code=True)["test"].shuffle(seed=seed)
     n_tune_data = 20
     tune_data = [
         {
@@ -334,7 +334,7 @@ def test_math(num_samples=-1):
         return
 
     seed = 41
-    data = datasets.load_dataset("competition_math")
+    data = datasets.load_dataset("competition_math", trust_remote_code=True)
     train_data = data["train"].shuffle(seed=seed)
     test_data = data["test"].shuffle(seed=seed)
     n_tune_data = 20