Add POS tagging and Phrase chunking token classification examples (#6457)

* Add more token classification examples

* POS tagging example

* Phrase chunking example

* PR review fixes

* Add conllu to third party list (used in token classification examples)

examples/token-classification/run_ner.py

@@ -14,16 +14,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ Fine-tuning the library models for named entity recognition on CoNLL-2003. """
-
-
 import logging
 import os
 import sys
 from dataclasses import dataclass, field
+from importlib import import_module
 from typing import Dict, List, Optional, Tuple
 
 import numpy as np
-from seqeval.metrics import f1_score, precision_score, recall_score
+from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score
 from torch import nn
 
 from transformers import (
@@ -36,7 +35,7 @@ from transformers import (
     TrainingArguments,
     set_seed,
 )
-from utils_ner import NerDataset, Split, get_labels
+from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
 
 
 logger = logging.getLogger(__name__)
@@ -54,6 +53,9 @@ class ModelArguments:
     config_name: Optional[str] = field(
         default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
     )
+    task_type: Optional[str] = field(
+        default="NER", metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}
+    )
     tokenizer_name: Optional[str] = field(
         default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
     )
@@ -113,6 +115,16 @@ def main():
             f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
         )
 
+    module = import_module("tasks")
+    try:
+        token_classification_task_clazz = getattr(module, model_args.task_type)
+        token_classification_task: TokenClassificationTask = token_classification_task_clazz()
+    except AttributeError:
+        raise ValueError(
+            f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
+            f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"
+        )
+
     # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
@@ -133,7 +145,7 @@ def main():
     set_seed(training_args.seed)
 
     # Prepare CONLL-2003 task
-    labels = get_labels(data_args.labels)
+    labels = token_classification_task.get_labels(data_args.labels)
     label_map: Dict[int, str] = {i: label for i, label in enumerate(labels)}
     num_labels = len(labels)
 
@@ -164,7 +176,8 @@ def main():
 
     # Get datasets
     train_dataset = (
-        NerDataset(
+        TokenClassificationDataset(
+            token_classification_task=token_classification_task,
             data_dir=data_args.data_dir,
             tokenizer=tokenizer,
             labels=labels,
@@ -177,7 +190,8 @@ def main():
         else None
     )
     eval_dataset = (
-        NerDataset(
+        TokenClassificationDataset(
+            token_classification_task=token_classification_task,
             data_dir=data_args.data_dir,
             tokenizer=tokenizer,
             labels=labels,
@@ -209,6 +223,7 @@ def main():
     def compute_metrics(p: EvalPrediction) -> Dict:
         preds_list, out_label_list = align_predictions(p.predictions, p.label_ids)
         return {
+            "accuracy_score": accuracy_score(out_label_list, preds_list),
             "precision": precision_score(out_label_list, preds_list),
             "recall": recall_score(out_label_list, preds_list),
             "f1": f1_score(out_label_list, preds_list),
@@ -253,7 +268,8 @@ def main():
 
     # Predict
     if training_args.do_predict:
-        test_dataset = NerDataset(
+        test_dataset = TokenClassificationDataset(
+            token_classification_task=token_classification_task,
             data_dir=data_args.data_dir,
             tokenizer=tokenizer,
             labels=labels,
@@ -278,19 +294,7 @@ def main():
         if trainer.is_world_master():
             with open(output_test_predictions_file, "w") as writer:
                 with open(os.path.join(data_args.data_dir, "test.txt"), "r") as f:
-                    example_id = 0
-                    for line in f:
-                        if line.startswith("-DOCSTART-") or line == "" or line == "\n":
-                            writer.write(line)
-                            if not preds_list[example_id]:
-                                example_id += 1
-                        elif preds_list[example_id]:
-                            output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
-                            writer.write(output_line)
-                        else:
-                            logger.warning(
-                                "Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]
-                            )
+                    token_classification_task.write_predictions_to_file(writer, f, preds_list)
 
     return results