NER - pl example (#3180)

* 1. seqeval required by ner pl example. install from examples/requirements. 2. unrecognized arguments: save_steps

* pl checkpoint callback filenotfound error: make directory and pass

* #3159 pl checkpoint path difference

* 1. Updated Readme for pl 2. pl script now also correct displays logs 3. pass gpu ids compared to number of gpus

* Updated results in readme

* 1. updated readme 2. removing deprecated pl methods 3. finalizing scripts

* comment length check

* using deprecated validation_end for stable results

* style related changes

examples/ner/run_pl_ner.py

@@ -141,10 +141,14 @@ class NERTransformer(BaseTransformer):
         return ret, preds_list, out_label_list
 
     def validation_end(self, outputs):
+        # todo: update to validation_epoch_end instead of deprecated validation_end
+        # when stable
         ret, preds, targets = self._eval_end(outputs)
-        return ret
+        logs = ret["log"]
+        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
 
-    def test_end(self, outputs):
+    def test_epoch_end(self, outputs):
+        # updating to test_epoch_end instead of deprecated test_end
         ret, predictions, targets = self._eval_end(outputs)
 
         if self.is_logger():
@@ -172,7 +176,12 @@ class NERTransformer(BaseTransformer):
                             logger.warning(
                                 "Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]
                             )
-        return ret
+        # Converting to the dic required by pl
+        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
+        # pytorch_lightning/trainer/logging.py#L139
+        logs = ret["log"]
+        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
+        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
 
     @staticmethod
     def add_model_specific_args(parser, root_dir):
@@ -217,6 +226,10 @@ if __name__ == "__main__":
     trainer = generic_train(model, args)
 
     if args.do_predict:
-        checkpoints = list(sorted(glob.glob(args.output_dir + "/checkpoint_*.ckpt", recursive=True)))
+        # See https://github.com/huggingface/transformers/issues/3159
+        # pl use this format to create a checkpoint:
+        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
+        # /pytorch_lightning/callbacks/model_checkpoint.py#L169
+        checkpoints = list(sorted(glob.glob(args.output_dir + "/checkpointepoch=*.ckpt", recursive=True)))
         NERTransformer.load_from_checkpoint(checkpoints[-1])
         trainer.test(model)