add frame list

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="transformer_srl",  # Replace with your own username
-    version="2.3",
+    version="2.4rc1",
     author="Riccardo Orlando",
     author_email="orlandoricc@gmail.com",
     description="SRL Transformer model",

transformer_srl/dataset_readers.py

@@ -1,8 +1,10 @@
 import logging
+import pathlib
 from typing import Any
 from typing import Dict, Tuple, List
 
 from allennlp.common.file_utils import cached_path
+from allennlp.data import Vocabulary
 from allennlp.data.dataset_readers.dataset_reader import DatasetReader
 from allennlp.data.fields import Field, TextField, SequenceLabelField, MetadataField
 from allennlp.data.instance import Instance
@@ -17,8 +19,13 @@
 from overrides import overrides
 from transformers import AutoTokenizer
 
+from transformer_srl.utils import load_label_list
+
 logger = logging.getLogger(__name__)
 
+FRAME_LIST_PATH = pathlib.Path(__file__).resolve().parent / "resources" / "framelist.txt"
+
+
 """
 ID: Word index, integer starting at 1 for each new sentence; may be a range for tokens with multiple words.
 FORM: Word form or punctuation symbol.
@@ -164,7 +171,6 @@ def __init__(
             "tokens": PretrainedTransformerIndexer(model_name)
         }
         self._domain_identifier = domain_identifier
-
         self.tokenizer = AutoTokenizer.from_pretrained(model_name)
         self.lowercase_input = "uncased" in model_name
 
@@ -232,19 +238,26 @@ def _read(self, file_path: str):
                 self._domain_identifier,
             )
 
-        for sentence in self._ontonotes_subset(
-            ontonotes_reader, file_path, self._domain_identifier
-        ):
-            tokens = [Token(t) for t in sentence.words]
-            if sentence.srl_frames:
-                for (_, tags) in sentence.srl_frames:
-                    verb_indicator = [1 if label[-2:] == "-V" else 0 for label in tags]
-                    frames = self._get_predicate_labels(sentence, verb_indicator)
-                    lemmas = [
-                        f for f, v in zip(sentence.predicate_lemmas, verb_indicator) if v == 1
-                    ]
-                    if not all(v == 0 for v in verb_indicator):
-                        yield self.text_to_instance(tokens, verb_indicator, frames, lemmas, tags)
+        counter = 0
+        try:
+            for sentence in self._ontonotes_subset(
+                ontonotes_reader, file_path, self._domain_identifier
+            ):
+                tokens = [Token(t) for t in sentence.words]
+                if sentence.srl_frames:
+                    for (_, tags) in sentence.srl_frames:
+                        verb_indicator = [1 if label[-2:] == "-V" else 0 for label in tags]
+                        frames = self._get_predicate_labels(sentence, verb_indicator)
+                        lemmas = [
+                            f for f, v in zip(sentence.predicate_lemmas, verb_indicator) if v == 1
+                        ]
+                        if not all(v == 0 for v in verb_indicator):
+                            yield self.text_to_instance(
+                                tokens, verb_indicator, frames, lemmas, tags
+                            )
+                counter += 1
+        except:
+            print("COUNTER", counter)
 
     def text_to_instance(  # type: ignore
         self,
@@ -353,16 +366,22 @@ def _convert_tags_to_wordpiece_tags(self, tags: List[str], offsets: List[int]) -
 
     def _get_predicate_labels(self, sentence, verb_indicator):
         labels = []
-        for i, v in enumerate(verb_indicator):
-            if v == 1:
-                label = (
-                    "{}.{}".format(sentence.predicate_lemmas[i], sentence.predicate_framenet_ids[i])
-                    if sentence.predicate_framenet_ids[i].isdigit()
-                    else sentence.predicate_framenet_ids[i]
-                )
-                labels.append(label)
-            else:
-                labels.append("O")
+        try:
+            for i, v in enumerate(verb_indicator):
+                if v == 1:
+                    label = (
+                        "{}.{}".format(
+                            sentence.predicate_lemmas[i], sentence.predicate_framenet_ids[i]
+                        )
+                        if sentence.predicate_framenet_ids[i].isdigit()
+                        else sentence.predicate_framenet_ids[i]
+                    )
+                    labels.append(label)
+                else:
+                    labels.append("O")
+        except:
+            print(sentence.words)
+            print(sentence.predicate_framenet_ids)
         return labels
 
 

transformer_srl/models.py

@@ -1,6 +1,4 @@
-import os
 import pathlib
-from collections import defaultdict
 from typing import Dict, List, Any, Union
 
 import numpy as np
@@ -9,27 +7,23 @@
 from allennlp.data import TextFieldTensors, Vocabulary
 from allennlp.models.model import Model
 from allennlp.nn import InitializerApplicator, util
-from allennlp.nn.util import (
-    get_lengths_from_binary_sequence_mask,
-    viterbi_decode,
-    get_device_of,
-)
+from allennlp.nn.util import get_device_of
 from allennlp.nn.util import get_text_field_mask, sequence_cross_entropy_with_logits
 from allennlp.training.metrics.fbeta_measure import FBetaMeasure
 from allennlp_models.structured_prediction import SrlBert
 from allennlp_models.structured_prediction.metrics.srl_eval_scorer import (
     DEFAULT_SRL_EVAL_PATH,
     SrlEvalScorer,
 )
-from allennlp.modules.token_embedders import PretrainedTransformerMismatchedEmbedder
 from overrides import overrides
 from torch.nn.modules import Linear, Dropout
 from transformers import AutoModel
 
-from transformer_srl.utils import load_lemma_frame, load_role_frame
+from transformer_srl.utils import load_lemma_frame, load_role_frame, load_label_list
 
 LEMMA_FRAME_PATH = pathlib.Path(__file__).resolve().parent / "resources" / "lemma2frame.csv"
 FRAME_ROLE_PATH = pathlib.Path(__file__).resolve().parent / "resources" / "frame2role.csv"
+FRAME_LIST_PATH = pathlib.Path(__file__).resolve().parent / "resources" / "framelist.txt"
 
 
 @Model.register("transformer_srl_dependency")
@@ -341,6 +335,9 @@ def __init__(
         else:
             self.transformer = bert_model
         self.frame_criterion = torch.nn.CrossEntropyLoss()
+        # add missing labels
+        frame_list = load_label_list(FRAME_LIST_PATH)
+        self.vocab.add_tokens_to_namespace(frame_list, "frames_labels")
         self.num_classes = self.vocab.get_vocab_size("labels")
         self.frame_num_classes = self.vocab.get_vocab_size("frames_labels")
         if srl_eval_path is not None:
@@ -569,8 +566,7 @@ def get_metrics(self, reset: bool = False):
             metric_dict_filtered = {
                 x.split("-")[0] + "_role": y
                 for x, y in metric_dict.items()
-                if "overall" in x
-                and "f1" in x
+                if "overall" in x and "f1" in x
             }
             frame_metric_dict = {
                 x + "_frame": y for x, y in frame_metric_dict.items() if "fscore" in x