add `dataset_name` to data_args and added accuracy metric (#11760)

* add `dataset_name` to data_args and added accuracy metric

* added documentation for dataset_name

* spelling correction

examples/pytorch/text-classification/README.md

@@ -22,8 +22,8 @@ Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/
 
 Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding
 Evaluation](https://gluebenchmark.com/). This script can fine-tune any of the models on the [hub](https://huggingface.co/models)
-and can also be used for your own data in a csv or a JSON file (the script might need some tweaks in that case, refer
-to the comments inside for help).
+and can also be used for a dataset hosted on our [hub](https://huggingface.co/datasets) or your own data in a csv or a JSON file 
+(the script might need some tweaks in that case, refer to the comments inside for help).
 
 GLUE is made up of a total of 9 different tasks. Here is how to run the script on one of them:
 
@@ -64,6 +64,22 @@ single Titan RTX was used):
 Some of these results are significantly different from the ones reported on the test set of GLUE benchmark on the
 website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the website.
 
+The following example fine-tunes BERT on the `imdb` dataset hosted on our [hub](https://huggingface.co/datasets):
+
+```bash
+python run_glue.py \
+  --model_name_or_path bert-base-cased \
+  --dataset_name imdb  \
+  --do_train \
+  --do_predict \
+  --max_seq_length 128 \
+  --per_device_train_batch_size 32 \
+  --learning_rate 2e-5 \
+  --num_train_epochs 3 \
+  --output_dir /tmp/imdb/
+```
+
+
 ### Mixed precision training
 
 If you have a GPU with mixed precision capabilities (architecture Pascal or more recent), you can use mixed precision

examples/pytorch/text-classification/run_glue.py

@@ -76,6 +76,12 @@ class DataTrainingArguments:
         default=None,
         metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())},
     )
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
     max_seq_length: int = field(
         default=128,
         metadata={
@@ -127,8 +133,10 @@ class DataTrainingArguments:
             self.task_name = self.task_name.lower()
             if self.task_name not in task_to_keys.keys():
                 raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys()))
+        elif self.dataset_name is not None:
+            pass
         elif self.train_file is None or self.validation_file is None:
-            raise ValueError("Need either a GLUE task or a training/validation file.")
+            raise ValueError("Need either a GLUE task, a training/validation file or a dataset name.")
         else:
             train_extension = self.train_file.split(".")[-1]
             assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
@@ -240,6 +248,9 @@ def main():
     if data_args.task_name is not None:
         # Downloading and loading a dataset from the hub.
         datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
+    elif data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
     else:
         # Loading a dataset from your local files.
         # CSV/JSON training and evaluation files are needed.
@@ -408,8 +419,8 @@ def main():
     # Get the metric function
     if data_args.task_name is not None:
         metric = load_metric("glue", data_args.task_name)
-    # TODO: When datasets metrics include regular accuracy, make an else here and remove special branch from
-    # compute_metrics
+    else:
+        metric = load_metric("accuracy")
 
     # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.