NER: parse args from .args file or JSON (#4110)

* ner: parse args from .args file or JSON

* examples: mention json-based configuration file support for run_ner script

examples/ner/README.md

@@ -79,6 +79,29 @@ python3 run_ner.py --data_dir ./ \
 
 If your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets.
 
+### JSON-based configuration file
+
+Instead of passing all parameters via commandline arguments, the `run_ner.py` script also supports reading parameters from a json-based configuration file:
+
+```json
+{
+    "data_dir": ".",
+    "labels": "./labels.txt",
+    "model_name_or_path": "bert-base-multilingual-cased",
+    "output_dir": "germeval-model",
+    "max_seq_length": 128,
+    "num_train_epochs": 3,
+    "per_gpu_train_batch_size": 32,
+    "save_steps": 750,
+    "seed": 1,
+    "do_train": true,
+    "do_eval": true,
+    "do_predict": true
+}
+```
+
+It must be saved with a `.json` extension and can be used by running `python3 run_ner.py config.json`.
+
 #### Evaluation
 
 Evaluation on development dataset outputs the following for our example:

examples/ner/run_ner.py

@@ -18,6 +18,7 @@
 
 import logging
 import os
+import sys
 from dataclasses import dataclass, field
 from typing import Dict, List, Optional, Tuple
 
@@ -94,6 +95,11 @@ def main():
     # We now keep distinct sets of args, for a cleaner separation of concerns.
 
     parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
     if (