Add cli argument for configuring labels

examples/run_ner.py

@@ -55,7 +55,7 @@ def set_seed(args):
         torch.cuda.manual_seed_all(args.seed)
 
 
-def train(args, train_dataset, model, tokenizer, pad_token_label_id):
+def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id):
     """ Train the model """
     if args.local_rank in [-1, 0]:
         tb_writer = SummaryWriter()
@@ -148,7 +148,7 @@ def train(args, train_dataset, model, tokenizer, pad_token_label_id):
                 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                     # Log metrics
                     if args.local_rank == -1 and args.evaluate_during_training:  # Only evaluate when single GPU otherwise metrics may not average well
-                        results = evaluate(args, model, tokenizer, pad_token_label_id)
+                        results = evaluate(args, model, tokenizer, labels, pad_token_label_id)
                         for key, value in results.items():
                             tb_writer.add_scalar("eval_{}".format(key), value, global_step)
                     tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
@@ -160,8 +160,7 @@ def train(args, train_dataset, model, tokenizer, pad_token_label_id):
                     output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
                     if not os.path.exists(output_dir):
                         os.makedirs(output_dir)
-                    model_to_save = model.module if hasattr(model,
-                                                            "module") else model  # Take care of distributed/parallel training
+                    model_to_save = model.module if hasattr(model, "module") else model  # Take care of distributed/parallel training
                     model_to_save.save_pretrained(output_dir)
                     torch.save(args, os.path.join(output_dir, "training_args.bin"))
                     logger.info("Saving model checkpoint to %s", output_dir)
@@ -179,8 +178,8 @@ def train(args, train_dataset, model, tokenizer, pad_token_label_id):
     return global_step, tr_loss / global_step
 
 
-def evaluate(args, model, tokenizer, pad_token_label_id, prefix=""):
-    eval_dataset = load_and_cache_examples(args, tokenizer, pad_token_label_id, evaluate=True)
+def evaluate(args, model, tokenizer, labels, pad_token_label_id, prefix=""):
+    eval_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, evaluate=True)
 
     args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
     # Note that DistributedSampler samples randomly
@@ -220,7 +219,7 @@ def evaluate(args, model, tokenizer, pad_token_label_id, prefix=""):
     eval_loss = eval_loss / nb_eval_steps
     preds = np.argmax(preds, axis=2)
 
-    label_map = {i: label for i, label in enumerate(get_labels())}
+    label_map = {i: label for i, label in enumerate(labels)}
 
     out_label_list = [[] for _ in range(out_label_ids.shape[0])]
     preds_list = [[] for _ in range(out_label_ids.shape[0])]
@@ -245,7 +244,7 @@ def evaluate(args, model, tokenizer, pad_token_label_id, prefix=""):
     return results
 
 
-def load_and_cache_examples(args, tokenizer, pad_token_label_id, evaluate=False):
+def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, evaluate=False):
     if args.local_rank not in [-1, 0] and not evaluate:
         torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
 
@@ -258,9 +257,8 @@ def load_and_cache_examples(args, tokenizer, pad_token_label_id, evaluate=False)
         features = torch.load(cached_features_file)
     else:
         logger.info("Creating features from dataset file at %s", args.data_dir)
-        label_list = get_labels()
         examples = read_examples_from_file(args.data_dir, evaluate=evaluate)
-        features = convert_examples_to_features(examples, label_list, args.max_seq_length, tokenizer,
+        features = convert_examples_to_features(examples, labels, args.max_seq_length, tokenizer,
                                                 cls_token_at_end=bool(args.model_type in ["xlnet"]),
                                                 # xlnet has a cls token at the end
                                                 cls_token=tokenizer.cls_token,
@@ -305,6 +303,8 @@ def main():
                         help="The output directory where the model predictions and checkpoints will be written.")
 
     ## Other parameters
+    parser.add_argument("--labels", default="", type=str,
+                        help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.")
     parser.add_argument("--config_name", default="", type=str,
                         help="Pretrained config name or path if not the same as model_name")
     parser.add_argument("--tokenizer_name", default="", type=str,
@@ -406,8 +406,8 @@ def main():
     set_seed(args)
 
     # Prepare CONLL-2003 task
-    label_list = get_labels()
-    num_labels = len(label_list)
+    labels = get_labels(args.labels)
+    num_labels = len(labels)
     # Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later
     pad_token_label_id = CrossEntropyLoss().ignore_index
 
@@ -433,8 +433,8 @@ def main():
 
     # Training
     if args.do_train:
-        train_dataset = load_and_cache_examples(args, tokenizer, pad_token_label_id, evaluate=False)
-        global_step, tr_loss = train(args, train_dataset, model, tokenizer, pad_token_label_id)
+        train_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, evaluate=False)
+        global_step, tr_loss = train(args, train_dataset, model, tokenizer, labels, pad_token_label_id)
         logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
 
     # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
@@ -466,7 +466,7 @@ def main():
             global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
             model = model_class.from_pretrained(checkpoint)
             model.to(args.device)
-            result = evaluate(args, model, tokenizer, pad_token_label_id, prefix=global_step)
+            result = evaluate(args, model, tokenizer, labels, pad_token_label_id, prefix=global_step)
             if global_step:
                 result = {"{}_{}".format(global_step, k): v for k, v in result.items()}
             results.update(result)

examples/utils_ner.py

@@ -202,5 +202,12 @@ def convert_examples_to_features(examples,
     return features
 
 
-def get_labels():
+def get_labels(path):
+    if path:
+        with open(path, "r") as f:
+            labels = f.read().splitlines()
+        if "O" not in labels:
+            labels = ["O"] + labels
+        return labels
+    else:
         return ["O", "B-MISC", "I-MISC",  "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]