diff --git a/.gitignore b/.gitignore index fcf6afd..9f1d7eb 100644 --- a/.gitignore +++ b/.gitignore @@ -132,4 +132,7 @@ dmypy.json # PyCharm .idea -*.sh \ No newline at end of file +*.sh + +# pytorch weights files +*.th diff --git a/gector/bert_token_embedder.py b/gector/bert_token_embedder.py index 327cd44..fcd7b6b 100644 --- a/gector/bert_token_embedder.py +++ b/gector/bert_token_embedder.py @@ -221,7 +221,7 @@ def forward( return util.uncombine_initial_dims(selected_embeddings, offsets.size()) -# @TokenEmbedder.register("bert-pretrained") +@TokenEmbedder.register("gec-bert-pretrained") class PretrainedBertEmbedder(BertEmbedder): """ diff --git a/gector/datareader.py b/gector/datareader.py index d511afb..5642f19 100644 --- a/gector/datareader.py +++ b/gector/datareader.py @@ -129,6 +129,9 @@ def text_to_instance(self, tokens: List[Token], tags: List[str] = None, fields: Dict[str, Field] = {} sequence = TextField(tokens, self._token_indexers) fields["tokens"] = sequence + # If words has not been explicitly passed in, create it from tokens. + if words is None: + words = [token.text for token in tokens] fields["metadata"] = MetadataField({"words": words}) if tags is not None: labels, detect_tags, complex_flag_dict = self.extract_tags(tags) diff --git a/gector/gec_model.py b/gector/gec_model.py index 7cc636b..63f30aa 100644 --- a/gector/gec_model.py +++ b/gector/gec_model.py @@ -39,6 +39,28 @@ def __init__(self, vocab_path=None, model_paths=None, del_confidence=0, resolve_cycles=False, ): + """ + Class used to enable prediction from GECToR model. + + Parameters + ---------- + vocab_path + model_paths: List[Path] + weigths + max_len + min_len + lowercase_tokens + log + iterations + model_name + special_tokens_fix + is_ensemble + min_error_probability + confidence + del_confidence + resolve_cycles: bool + This parameter is unused. + """ self.model_weights = list(map(float, weigths)) if weigths else [1] * len(model_paths) self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") self.max_len = max_len diff --git a/gector/gec_predictor.py b/gector/gec_predictor.py new file mode 100644 index 0000000..fa5c5a1 --- /dev/null +++ b/gector/gec_predictor.py @@ -0,0 +1,182 @@ +from typing import Dict, List + +import numpy +from allennlp.predictors import Predictor +from allennlp.models import Model +from allennlp.common.util import sanitize +from overrides import overrides +from allennlp.common.util import JsonDict +from allennlp.data import DatasetReader, Instance, Token +from allennlp.data.tokenizers.word_splitter import JustSpacesWordSplitter +from allennlp.models import Model +from utils.helpers import START_TOKEN + + +@Predictor.register("gec-predictor") +class GecPredictor(Predictor): + """ + A Predictor for generating predictions from GECToR. + + Note that currently, this is unable to handle ensemble predictions. + """ + + def __init__(self, + model: Model, + dataset_reader: DatasetReader, + iterations: int = 3) -> None: + """ + Parameters + --------- + model: Model + An instantiated `Seq2Labels` model for performing grammatical error correction. + dataset_reader: DatasetReader + An instantiated dataset reader, typically `Seq2LabelsDatasetReader`. + iterations: int + This represents the number of times grammatical error correction is applied to the input. + """ + super().__init__(model, dataset_reader) + self._tokenizer = JustSpacesWordSplitter() + self._iterations = iterations + + def predict(self, sentence: str) -> JsonDict: + """ + Generate error correction predictions for a single input string. + + Parameters + ---------- + sentence: str + The input text to perform error correction on. + + Returns + ------- + JsonDict + A dictionary containing the following keys: + - logits_labels + - logits_d_tags + - class_probabilities_labels + - class_probabilities_d_tags + - max_error_probability + - words + - labels + - d_tags + - corrected_words + For an explanation of each of these see `Seq2Labels.decode()`. + """ + return self.predict_json({"sentence": sentence}) + + def predict_batch(self, sentences: List[str]) -> List[JsonDict]: + """ + Generate predictions for a sequence of input strings. + + Parameters + ---------- + sentences: List[str] + A list of strings to correct. + + Returns + ------- + List[JsonDict] + A list of dictionaries, each containing the following keys: + - logits_labels + - logits_d_tags + - class_probabilities_labels + - class_probabilities_d_tags + - max_error_probability + - words + - labels + - d_tags + - corrected_words + For an explanation of each of these see `Seq2Labels.decode()`. + """ + return self.predict_batch_json([{"sentence": sentence} for sentence in sentences]) + + @overrides + def predict_instance(self, instance: Instance) -> JsonDict: + """ + This special predict_instance method allows for applying the correction model multiple times. + + Parameters + --------- + + Returns + ------- + JsonDict + A dictionary containing the following keys: + - logits_labels + - logits_d_tags + - class_probabilities_labels + - class_probabilities_d_tags + - max_error_probability + - words + - labels + - d_tags + - corrected_words + For an explanation of each of these see `Seq2Labels.decode()`. + """ + for i in range(self._iterations): + output = self._model.forward_on_instance(instance) + # integrate predictions back into instance for next iteration + tokens = [Token(word) for word in output["corrected_words"]] + instance = self._dataset_reader.text_to_instance(tokens) + return sanitize(output) + + @overrides + def predict_batch_instance(self, instances: List[Instance]) -> List[JsonDict]: + """ + This special predict_batch_instance method allows for applying the correction model multiple times. + + Parameters + ---------- + + Returns + ------- + List[JsonDict] + A list of dictionaries, each containing the following keys: + - logits_labels + - logits_d_tags + - class_probabilities_labels + - class_probabilities_d_tags + - max_error_probability + - words + - labels + - d_tags + - corrected_words + For an explanation of each of these see `Seq2Labels.decode()`. + """ + for i in range(self._iterations): + outputs = self._model.forward_on_instances(instances) + corrected_instances = [] + for output in outputs: + tokens = [Token(word) for word in output["corrected_words"]] + instance = self._dataset_reader.text_to_instance(tokens) + corrected_instances.append(instance) + instances = corrected_instances + return sanitize(outputs) + + @overrides + def _json_to_instance(self, json_dict: JsonDict) -> Instance: + """ + Convert a JsonDict into an Instance. + + This is used internally by `self.predict_json()`. + + Parameters + ---------- + json_dict: JsonDict + Expects a dict with a single key "sentence" with a value representing the string to correct. + i.e. ``{"sentence": "..."}``. + + Returns + ------ + Instance + An instance with the following fields: + - tokens + - metadata + - labels + - d_tags + """ + sentence = json_dict["sentence"] + tokens = self._tokenizer.split_words(sentence) + # Add start token to front + tokens = [Token(START_TOKEN)] + tokens + return self._dataset_reader.text_to_instance(tokens) diff --git a/gector/seq2labels_model.py b/gector/seq2labels_model.py index cdd6f59..b5feb9f 100644 --- a/gector/seq2labels_model.py +++ b/gector/seq2labels_model.py @@ -12,6 +12,7 @@ from allennlp.training.metrics import CategoricalAccuracy from overrides import overrides from torch.nn.modules.linear import Linear +from utils.helpers import PAD, UNK, get_target_sent_by_edits, START_TOKEN @Model.register("seq2labels") @@ -60,6 +61,7 @@ def __init__(self, vocab: Vocabulary, label_smoothing: float = 0.0, confidence: float = 0.0, del_confidence: float = 0.0, + min_error_probability: float = 0.0, initializer: InitializerApplicator = InitializerApplicator(), regularizer: Optional[RegularizerApplicator] = None) -> None: super(Seq2Labels, self).__init__(vocab, regularizer) @@ -72,6 +74,7 @@ def __init__(self, vocab: Vocabulary, self.label_smoothing = label_smoothing self.confidence = confidence self.del_conf = del_confidence + self.min_error_probability = min_error_probability self.incorr_index = self.vocab.get_token_index("INCORRECT", namespace=detect_namespace) @@ -161,7 +164,9 @@ def forward(self, # type: ignore output_dict["loss"] = loss_labels + loss_d if metadata is not None: - output_dict["words"] = [x["words"] for x in metadata] + output_dict["words"] = [] + for instance in metadata: + output_dict["words"].append([word for word in instance["words"] if word != START_TOKEN]) return output_dict @overrides @@ -169,6 +174,46 @@ def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor """ Does a simple position-wise argmax over each token, converts indices to string labels, and adds a ``"tags"`` key to the dictionary with the result. + + Parameters + ---------- + output_dict: Dict[str, torch.Tensor] + This is expected to have the following keys: + - logits_labels + - logits_d_tags + - class_probabilities_labels + - class_probabilities_d_tags + - max_error_probability + - words + + Returns + ------ + Dict + A dictionary containing the following keys: + - logits_labels + Logits for labels indicating the types of corrections + to perform. + - logits_d_tags + Logits for labels indicating the presence or absence + of grammatical errors. + - class_probabilities_labels + Class probabilities for labels indicating the types + of corrections to perform. + - class_probabilities_d_tags + Class probabilities for labels indicating the presence + or absence of grammatical errors. + - max_error_probability + A threshold probability that has to be exceeded for an + error to be corrected. + - words + The original tokens used to create the instance. + - labels + Labels indicating the types of corrections to perform. + - d_tags + Labels indicating the presence or absence of grammatical errors. + - corrected_words + `words` after applying the correction operations + specified in `labels` """ for label_namespace in self.label_namespaces: all_predictions = output_dict[f'class_probabilities_{label_namespace}'] @@ -185,8 +230,58 @@ def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor for x in argmax_indices] all_tags.append(tags) output_dict[f'{label_namespace}'] = all_tags + batch_size = len(output_dict['labels']) + output_dict['corrected_words'] = [] + for i in range(batch_size): + words_in_instance = output_dict['words'][i] + batch_len = len(words_in_instance) + probs = output_dict['class_probabilities_labels'][i] + max_probs = torch.max(probs, dim=0) + probs = max_probs[0].tolist() + indices = max_probs[1].tolist() + if max(indices) == 0: # No corrections should be performed + output_dict["corrected_words"].append(output_dict["words"][i]) + else: + actions_per_token = [] + for j in range(batch_len): + if j == 0: + token = START_TOKEN + else: + token = words_in_instance[j] + if indices[j] == 0: + continue + suggested_token_operation = output_dict['labels'][i][j] + action = self.get_token_action(index=j, prob=probs[j], + sugg_token=suggested_token_operation) + if not action: + continue + actions_per_token.append(action) + corrected_sent = get_target_sent_by_edits(output_dict['words'][i], actions_per_token) + output_dict['corrected_words'].append(corrected_sent) return output_dict + def get_token_action(self, index, prob, sugg_token): + """Get list of suggested actions for token.""" + # cases when we don't need to do anything + if prob < self.min_error_probability or sugg_token in [UNK, PAD, '$KEEP']: + return None + + if sugg_token.startswith('$REPLACE_') or sugg_token.startswith('$TRANSFORM_') or sugg_token == '$DELETE': + start_pos = index + end_pos = index + 1 + elif sugg_token.startswith("$APPEND_") or sugg_token.startswith("$MERGE_"): + start_pos = index + 1 + end_pos = index + 1 + + if sugg_token == "$DELETE": + sugg_token_clear = "" + elif sugg_token.startswith('$TRANSFORM_') or sugg_token.startswith("$MERGE_"): + sugg_token_clear = sugg_token[:] + else: + sugg_token_clear = sugg_token[sugg_token.index('_') + 1:] + + return start_pos - 1, end_pos - 1, sugg_token_clear, prob + @overrides def get_metrics(self, reset: bool = False) -> Dict[str, float]: metrics_to_return = {metric_name: metric.get_metric(reset) for diff --git a/gector/tokenizer_indexer.py b/gector/tokenizer_indexer.py index 4f086d3..7ed2a5b 100644 --- a/gector/tokenizer_indexer.py +++ b/gector/tokenizer_indexer.py @@ -101,6 +101,7 @@ def get_keys(self, index_name: str) -> List[str]: return [index_name, f"{index_name}-offsets", f"{index_name}-type-ids", "mask"] +@TokenIndexer.register("gec-pretrained-bert-indexer") class PretrainedBertIndexer(TokenizerIndexer): # pylint: disable=line-too-long """ diff --git a/test_fixtures/roberta_model/config.json b/test_fixtures/roberta_model/config.json new file mode 100644 index 0000000..6b63071 --- /dev/null +++ b/test_fixtures/roberta_model/config.json @@ -0,0 +1,61 @@ +{ + "dataset_reader": { + "type": "seq2labels_datareader", + "max_len": 50, + "skip_correct": true, + "skip_complex": 0, + "test_mode": false, + "tag_strategy": "keep_one", + "broken_dot_strategy": "keep", + "lazy": true, + "tn_prob": 0, + "tp_prob": 1, + "token_indexers": { + "bert": { + "type": "gec-pretrained-bert-indexer", + "pretrained_model": "roberta-base", + "max_pieces_per_token": 5, + "do_lowercase": false, + "special_tokens_fix": 1 + } + } + }, + "iterator": { + "type": "bucket", + "batch_size": 32, + "sorting_keys": [ + [ + "tokens", + "num_tokens" + ] + ] + }, + "model": { + "type": "seq2labels", + "text_field_embedder": { + "allow_unmatched_keys": true, + "embedder_to_indexer_map": {"bert": ["bert", "bert-offsets"]}, + "token_embedders": { + "bert": { + "type": "gec-bert-pretrained", + "requires_grad": false, + "top_layer_only": true, + "pretrained_model": "roberta-base", + "special_tokens_fix": 1 + } + } + } + }, + "trainer": { + "cuda_device": 0, + "grad_clipping": 5, + "num_epochs": 50, + "optimizer": { + "type": "adagrad", + "lr": 0.001 + }, + "patience": 10, + "validation_metric": "-loss" + }, + "evaluate_on_test": true +} \ No newline at end of file diff --git a/test_fixtures/roberta_model/vocabulary/d_tags.txt b/test_fixtures/roberta_model/vocabulary/d_tags.txt new file mode 100644 index 0000000..56e0504 --- /dev/null +++ b/test_fixtures/roberta_model/vocabulary/d_tags.txt @@ -0,0 +1,4 @@ +CORRECT +INCORRECT +@@UNKNOWN@@ +@@PADDING@@ diff --git a/test_fixtures/roberta_model/vocabulary/labels.txt b/test_fixtures/roberta_model/vocabulary/labels.txt new file mode 100644 index 0000000..0086d2a --- /dev/null +++ b/test_fixtures/roberta_model/vocabulary/labels.txt @@ -0,0 +1,5002 @@ +$KEEP +$DELETE +$TRANSFORM_CASE_CAPITAL +$APPEND_the +$APPEND_, +$APPEND_a +$TRANSFORM_VERB_VB_VBZ +$TRANSFORM_AGREEMENT_PLURAL +$TRANSFORM_CASE_LOWER +$TRANSFORM_VERB_VB_VBN +$REPLACE_the +$REPLACE_a +$REPLACE_to +$TRANSFORM_VERB_VB_VBG +$REPLACE_. +$APPEND_to +$REPLACE_, +$REPLACE_in +$REPLACE_was +$TRANSFORM_VERB_VBZ_VB +$TRANSFORM_AGREEMENT_SINGULAR +$APPEND_I +$APPEND_. +$REPLACE_for +$REPLACE_I +$APPEND_( +$TRANSFORM_VERB_VBG_VB +$REPLACE_is +$REPLACE_have +$REPLACE_on +$REPLACE_are +$REPLACE_of +$REPLACE_it +$TRANSFORM_VERB_VBN_VB +$REPLACE_that +$APPEND_in +$REPLACE_and +$APPEND_it +$APPEND_that +$REPLACE_at +$APPEND_for +$APPEND_of +$APPEND_and +$REPLACE_an +$REPLACE_my +$REPLACE_had +$APPEND_have +$APPEND_an +$REPLACE_has +$APPEND_my +$APPEND_is +$APPEND_The +$APPEND_will +$REPLACE_with +$REPLACE_were +$REPLACE_be +$TRANSFORM_VERB_VBN_VBG +$APPEND_`` +$REPLACE_do +$TRANSFORM_VERB_VBG_VBN +$REPLACE_this +$REPLACE_will +$TRANSFORM_VERB_VB_VBD +$APPEND_was +$REPLACE_n't +$APPEND_about +$REPLACE_from +$REPLACE_about +$REPLACE_It +$APPEND_on +$REPLACE_would +$MERGE_SPACE +$APPEND_at +$APPEND_'s +$REPLACE_as +$REPLACE_'s +$REPLACE_could +$APPEND_with +$REPLACE_did +$REPLACE_them +$REPLACE_The +$REPLACE_by +$REPLACE_so +$REPLACE_not +$REPLACE_can +$APPEND_am +$APPEND_be +$REPLACE_because +$APPEND_/ +$REPLACE_they +$REPLACE_am +$APPEND_are +$TRANSFORM_VERB_VBZ_VBN +$REPLACE_'m +$REPLACE_their +$TRANSFORM_VERB_VBN_VBZ +$APPEND_had +$APPEND_would +$APPEND_- +$REPLACE_( +$TRANSFORM_VERB_VBN_VBD +$REPLACE_very +$REPLACE_people +$REPLACE_get +$REPLACE_there +$REPLACE_? +$APPEND_do +$REPLACE_; +$REPLACE_me +$REPLACE_one +$REPLACE_been +$APPEND_so +$APPEND_) +$APPEND_'m +$REPLACE_or +$REPLACE_some +$REPLACE_you +$TRANSFORM_VERB_VBD_VBN +$APPEND_as +$REPLACE_like +$TRANSFORM_VERB_VBD_VB +$REPLACE_which +$APPEND_has +$REPLACE_these +$REPLACE_This +$APPEND_from +$REPLACE_when +$APPEND_'ve +$REPLACE_`` +$APPEND_there +$REPLACE_does +$APPEND_also +$APPEND_It +$APPEND_can +$REPLACE_: +$REPLACE_other +$APPEND_more +$REPLACE_want +$REPLACE_we +$REPLACE_'ve +$REPLACE_what +$REPLACE_more +$REPLACE_many +$REPLACE_into +$APPEND_been +$APPEND_by +$APPEND_this +$REPLACE_went +$REPLACE_time +$APPEND_only +$TRANSFORM_VERB_VBG_VBZ +$REPLACE_go +$REPLACE_while +$REPLACE_but +$APPEND_all +$APPEND_if +$REPLACE_should +$REPLACE_out +$APPEND_' +$REPLACE_during +$REPLACE_much +$APPEND_like +$REPLACE_! +$APPEND_but +$REPLACE_if +$REPLACE_since +$APPEND_people +$APPEND_because +$REPLACE_any +$APPEND_A +$REPLACE_another +$REPLACE_They +$APPEND_you +$REPLACE_ca +$REPLACE_our +$REPLACE_who +$APPEND_now +$REPLACE_really +$REPLACE_make +$APPEND_me +$APPEND_who +$REPLACE_In +$REPLACE_her +$REPLACE_English +$APPEND_some +$APPEND_when +$APPEND_still +$APPEND_them +$REPLACE_use +$APPEND_just +$REPLACE_things +$REPLACE_/ +$REPLACE_got +$REPLACE_My +$APPEND_were +$REPLACE_he +$REPLACE_countries +$APPEND_their +$REPLACE_using +$TRANSFORM_VERB_VBZ_VBG +$APPEND_'ll +$REPLACE_being +$REPLACE_too +$APPEND_we +$APPEND_they +$REPLACE_lot +$REPLACE_- +$REPLACE_all +$REPLACE_good +$APPEND_[ +$REPLACE_every +$REPLACE_) +$REPLACE_your +$APPEND_My +$APPEND_even +$APPEND_out +$REPLACE_his +$REPLACE_made +$APPEND_any +$REPLACE_where +$APPEND_which +$REPLACE_work +$REPLACE_used +$APPEND_one +$REPLACE_take +$APPEND_In +$REPLACE_There +$REPLACE_up +$REPLACE_how +$REPLACE_myself +$APPEND_what +$APPEND_very +$APPEND_? +$REPLACE_become +$REPLACE_think +$REPLACE_going +$REPLACE_Japanese +$REPLACE_well +$APPEND_being +$APPEND_or +$REPLACE_just +$REPLACE_write +$REPLACE_those +$REPLACE_feel +$REPLACE_until +$APPEND_However +$APPEND_our +$REPLACE_something +$APPEND_get +$REPLACE_diary +$REPLACE_no +$REPLACE_over +$APPEND_time +$APPEND_then +$REPLACE_see +$REPLACE_writing +$REPLACE_wo +$REPLACE_only +$REPLACE_'ll +$REPLACE_after +$REPLACE_know +$REPLACE_anything +$REPLACE_now +$REPLACE_That +$REPLACE_first +$REPLACE_than +$APPEND_up +$REPLACE_better +$REPLACE_hope +$REPLACE_through +$REPLACE_doing +$APPEND_go +$REPLACE_then +$APPEND_too +$REPLACE_studying +$REPLACE_its +$REPLACE_learn +$REPLACE_lives +$REPLACE_having +$REPLACE_told +$REPLACE_What +$REPLACE_she +$REPLACE_thought +$APPEND_not +$REPLACE_around +$REPLACE_him +$REPLACE_different +$APPEND_could +$APPEND_such +$REPLACE_able +$REPLACE_On +$REPLACE_before +$REPLACE_though +$REPLACE_also +$APPEND_entry +$REPLACE_learned +$TRANSFORM_CASE_UPPER +$APPEND_again +$REPLACE_friends +$APPEND_This +$REPLACE_might +$REPLACE_A +$REPLACE_However +$APPEND_really +$REPLACE_started +$REPLACE_improve +$APPEND_English +$REPLACE_years +$REPLACE_' +$REPLACE_most +$APPEND_how +$REPLACE_day +$APPEND_: +$APPEND_today +$REPLACE_find +$REPLACE_help +$APPEND_should +$REPLACE_We +$REPLACE_even +$REPLACE_may +$REPLACE_left +$REPLACE_called +$APPEND_did +$REPLACE_course +$REPLACE_These +$REPLACE_understand +$REPLACE_So +$REPLACE_said +$REPLACE_took +$REPLACE_person +$REPLACE_school +$REPLACE_such +$APPEND_called +$REPLACE_At +$APPEND_before +$REPLACE_way +$APPEND_he +$REPLACE_everyone +$REPLACE_here +$REPLACE_When +$REPLACE_everything +$REPLACE_need +$APPEND_her +$REPLACE_Because +$TRANSFORM_VERB_VBD_VBG +$REPLACE_say 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+$REPLACE_snowboard +$REPLACE_novel +$REPLACE_predict +$REPLACE_remained +$REPLACE_outcome +$APPEND_whose +$APPEND_slightly +$APPEND_serious +$REPLACE_Research +$REPLACE_marvelous +$APPEND_excited +$REPLACE_organization +$REPLACE_list +$REPLACE_automatically +$REPLACE_differ +$REPLACE_Mount +$REPLACE_arrangement +$APPEND_spending +$REPLACE_adopt +$APPEND_Soon +$APPEND_Mr +$REPLACE_irritable +$REPLACE_Wish +$REPLACE_writting +$REPLACE_Sincerely +$APPEND_winter +$REPLACE_rose +$REPLACE_businessman +$REPLACE_flavors +$REPLACE_smell +$REPLACE_fortunate +$APPEND_TOEIC +$APPEND_mentioned +$APPEND_process +$APPEND_amp +$APPEND_neither +$REPLACE_enemies +$REPLACE_acceptance +$REPLACE_drivers +$REPLACE_murderer +$REPLACE_Melbourne +$REPLACE_Specifically +$APPEND_complete +$APPEND_focus +$REPLACE_illegal +$APPEND_hurts +$REPLACE_groom +$APPEND_preposition +$APPEND_com +$APPEND_beautiful +$REPLACE_sightseeing +$REPLACE_bringing +$REPLACE_sources +$APPEND_videos +$APPEND_lunch +$APPEND_11 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+$REPLACE_encouragement +$REPLACE_regard +$REPLACE_slower +$REPLACE_dolls +$REPLACE_200 +$REPLACE_unconsciously +$APPEND_happens +$REPLACE_facility +$APPEND_advice +$REPLACE_North +$REPLACE_awareness +$APPEND_planned +$REPLACE_genetic +$REPLACE_management +$REPLACE_refund +$REPLACE_brighter +$REPLACE_confirm +$REPLACE_burning +$REPLACE_composition +$APPEND_answer +$REPLACE_conserve +$REPLACE_destruction +$REPLACE_duties +$REPLACE_creativity +$APPEND_expressions +$APPEND_commit +$REPLACE_East +$REPLACE_milk +$REPLACE_30pm +$REPLACE_belong +$REPLACE_autograph +$REPLACE_caring +$REPLACE_download +$APPEND_development +$REPLACE_compete +$REPLACE_qualities +$APPEND_avoid +$REPLACE_recieved +$APPEND_Perfect +$REPLACE_yours +$REPLACE_breaks +$REPLACE_amusement +$REPLACE_models +$REPLACE_persevere +$REPLACE_emergency +$REPLACE_empty +$REPLACE_rescue +$APPEND_term +$REPLACE_requirements +$REPLACE_sufficient +$APPEND_cooking +$REPLACE_fascinated +$REPLACE_14th +$REPLACE_relevant +$REPLACE_listed +$REPLACE_vision +$REPLACE_g +$REPLACE_leadership +$REPLACE_butI +$APPEND_provide +$REPLACE_organize +$APPEND_created +$REPLACE_12th +$REPLACE_collection +$REPLACE_supply +$APPEND_Besides +$REPLACE_stranger +$REPLACE_combination +$REPLACE_farther +$REPLACE_awaiting +$APPEND_hand +$REPLACE_unsure +$REPLACE_profile +$APPEND_moving +$APPEND_street +$REPLACE_delighted +$REPLACE_pretended +$REPLACE_driven +$REPLACE_maintaining +$REPLACE_liar +$TRANSFORM_SPLIT_HYPHEN +$REPLACE_glass +$REPLACE_stick +$REPLACE_itchy +$REPLACE_ought +$REPLACE_consumption +$REPLACE_quicker +$REPLACE_spare +$REPLACE_governments +$APPEND_view +$REPLACE_P +$REPLACE_colorful +$REPLACE_guitarist +$APPEND_wants +$REPLACE_million +$REPLACE_behalf +$REPLACE_kilometres +$REPLACE_bank +$APPEND_morning +$REPLACE_weekends +$REPLACE_occasion +$APPEND_tour +$REPLACE_object +$REPLACE_Others +$REPLACE_Considering +$REPLACE_species +$REPLACE_session +$APPEND_removed +$REPLACE_hiking +$REPLACE_resolutions +$REPLACE_peak +$REPLACE_consequences +$REPLACE_soaked +$REPLACE_presents +$APPEND_25 +$REPLACE_salad +$REPLACE_filling +$REPLACE_attack +$APPEND_foods +$REPLACE_tendency +$REPLACE_discoveries +$REPLACE_immediate +$REPLACE_submitted +$REPLACE_THAT +$APPEND_develop +$REPLACE_battery +$REPLACE_dont +$REPLACE_feature +$APPEND_opportunity +$REPLACE_bodies +$REPLACE_goldfish +$REPLACE_adapt +$REPLACE_views +$REPLACE_forgetting +$REPLACE_saved +$REPLACE_doesn +$REPLACE_thirst +$APPEND_Me +$REPLACE_distant +$REPLACE_opposition +$REPLACE_breed +$REPLACE_practised +$REPLACE_miserable +$APPEND_sore +$REPLACE_brain +$REPLACE_sessions +$REPLACE_policeman +$REPLACE_favor +$REPLACE_managing +$REPLACE_rains +$REPLACE_baths +$REPLACE_surrounding +$REPLACE_Seoul +$APPEND_regardless +$APPEND_Something +$REPLACE_architectural +$REPLACE_ok +$REPLACE_welfare +$APPEND_share +$REPLACE_daughters +$REPLACE_phones +$REPLACE_downstairs +$REPLACE_arriving +$REPLACE_stepped +$REPLACE_competing +$REPLACE_catching +$REPLACE_conversing +$REPLACE_encourages +$REPLACE_depressing +$REPLACE_begining +$REPLACE_admission +$APPEND_voice +$REPLACE_boredom +$APPEND_alot +$APPEND_familiar +$REPLACE_breaking +$REPLACE_fortunately +$REPLACE_Over +$APPEND_lost +$REPLACE_intended +$REPLACE_neighbourhood +$REPLACE_mysteries +$REPLACE_certificate +$REPLACE_data +$APPEND_personal +$REPLACE_joyful +$REPLACE_immigrants +$REPLACE_emotions +$REPLACE_checkup +$REPLACE_licence +$REPLACE_juice +$APPEND_whenever +$REPLACE_dogs +$REPLACE_thereby +$APPEND_department +$APPEND_assignment +$REPLACE_defend +$REPLACE_approached +$REPLACE_Fireworks +$APPEND_activity +$APPEND_quality +$REPLACE_basics +$REPLACE_costumes +$REPLACE_key +$REPLACE_outdoors +$REPLACE_hay +$APPEND_prepare +$REPLACE_hiding +$REPLACE_curiosity +$APPEND_dealing +$REPLACE_passion +$REPLACE_costed +$REPLACE_fries +$REPLACE_HAVE +$REPLACE_divorced +$APPEND_display +$REPLACE_baby +$APPEND_cherry +$REPLACE_Returning +$APPEND_lack +$APPEND_learnt +$REPLACE_Im +$APPEND_naturally +$REPLACE_router +$APPEND_goals +$REPLACE_seaside +$REPLACE_summarize +$APPEND_appeared +$REPLACE_claim +$APPEND_ate +$REPLACE_exchanging +$APPEND_arrive +$APPEND_art +$REPLACE_participating +$REPLACE_seek +$REPLACE_innocent +$APPEND_express +$REPLACE_lunchtime +$REPLACE_reaction +$REPLACE_consisted +$REPLACE_Eastern +$APPEND_track +$APPEND_baby +$REPLACE_touching +$REPLACE_lively +$REPLACE_bridge +$REPLACE_murderers +$REPLACE_Brazil +$REPLACE_feeding +$REPLACE_honestly +$REPLACE_Piece +$REPLACE_springs +$REPLACE_purchase +$REPLACE_pray +$REPLACE_washed +$APPEND_sentence +$REPLACE_Olympics +$REPLACE_strongest +$REPLACE_leads +$REPLACE_stomachache +$REPLACE_John +$REPLACE_opponent +$REPLACE_contents +$REPLACE_plot +$APPEND_Many +$REPLACE_experiment +$REPLACE_beings +$REPLACE_owns +$REPLACE_airline +$REPLACE_severely +$REPLACE_ages +@@UNKNOWN@@ +@@PADDING@@ diff --git a/test_fixtures/roberta_model/vocabulary/non_padded_namespaces.txt b/test_fixtures/roberta_model/vocabulary/non_padded_namespaces.txt new file mode 100644 index 0000000..d5dee50 --- /dev/null +++ b/test_fixtures/roberta_model/vocabulary/non_padded_namespaces.txt @@ -0,0 +1,2 @@ +*tags +*labels diff --git a/tests/test_gec_model.py b/tests/test_gec_model.py index 8260c6b..0056c59 100644 --- a/tests/test_gec_model.py +++ b/tests/test_gec_model.py @@ -25,7 +25,7 @@ def setUp(self): self.model_name = "roberta-base" model_url = "https://grammarly-nlp-data-public.s3.amazonaws.com/gector/roberta_1_gectorv2.th" test_fixtures_dir_path = Path(__file__).parent.parent / "test_fixtures" - model_path = test_fixtures_dir_path / "roberta_1_gectorv2.th" + model_path = test_fixtures_dir_path / "roberta_model" / "weights.th" if not model_path.exists(): response = requests.get(model_url) with model_path.open("wb") as out_fp: diff --git a/tests/test_gec_predictor.py b/tests/test_gec_predictor.py new file mode 100644 index 0000000..537a996 --- /dev/null +++ b/tests/test_gec_predictor.py @@ -0,0 +1,50 @@ +from pathlib import Path +import torch +import requests +from tqdm import tqdm + +from allennlp.common.testing import ModelTestCase +from allennlp.predictors import Predictor + +# These imports are required so that instantiating the predictor can be done automatically +from gector.datareader import Seq2LabelsDatasetReader +from gector.seq2labels_model import Seq2Labels +from gector.bert_token_embedder import PretrainedBertEmbedder +from gector.tokenizer_indexer import PretrainedBertIndexer +from gector.gec_predictor import GecPredictor + + +class TestGecPredictor(ModelTestCase): + """Test class for GecModel""" + + def setUp(self): + super().setUp() + # Download weights for model archive + weights_url = "https://grammarly-nlp-data-public.s3.amazonaws.com/gector/roberta_1_gectorv2.th" + test_fixtures_dir_path = Path(__file__).parent.parent / "test_fixtures" + model_path = test_fixtures_dir_path / "roberta_model" / "weights.th" + if not model_path.exists(): + response = requests.get(weights_url) + with model_path.open("wb") as out_fp: + # Write out data with progress bar + for data in tqdm(response.iter_content()): + out_fp.write(data) + model_path = test_fixtures_dir_path / "roberta_model" + self.model_path = model_path + sentence1 = "I run to a stores every day." + sentence2 = "the quick brown foxes jumps over a elmo's laziest friend" + # This mimics how batches of requests are constructed in predict.py's predict_for_file function + self.input_data = [sentence1, sentence2] + + def test_gec_predictor_prediction(self): + """Test simple prediction integration test with GecPredictor""" + gec_model = Predictor.from_path(self.model_path, predictor_name="gec-predictor") + prediction = gec_model.predict_batch(self.input_data) + # subject verb agreement is not fixed in the second sentence when predicting using GecModel + # (i.e.) brown foxes jump + assert set(prediction[0].keys()) == {'logits_labels', 'logits_d_tags', 'class_probabilities_labels', + 'class_probabilities_d_tags', 'max_error_probability', 'words', 'labels', + 'd_tags', 'corrected_words'} + assert prediction[0]["corrected_words"] == ['I', 'run', 'to', 'the', 'stores', 'every', 'day.'] + assert prediction[1]["corrected_words"] == ['The', 'quick', 'brown', 'foxes', 'jumps', 'over', "Elmo's", + 'laziest', 'friend'] diff --git a/tests/test_seq2labels.py b/tests/test_seq2labels.py index 877786f..60d559c 100644 --- a/tests/test_seq2labels.py +++ b/tests/test_seq2labels.py @@ -4,10 +4,12 @@ from allennlp.common.testing import ModelTestCase from allennlp.data.dataset import Batch from allennlp.data.fields import TextField +from allennlp.data.fields import MetadataField from allennlp.data.instance import Instance from allennlp.data import Token from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder from allennlp.data.vocabulary import Vocabulary +from allennlp.nn.util import move_to_device from gector.bert_token_embedder import PretrainedBertEmbedder from gector.tokenizer_indexer import PretrainedBertIndexer @@ -49,18 +51,23 @@ def setUp(self): self.device = torch.device( "cuda:0" if torch.cuda.is_available() else "cpu" ) + self.device_index = 0 if torch.cuda.is_available() else -1 sentence1 = "the quickest quick brown fox jumped over the lazy dog" - tokens1 = [Token(word) for word in sentence1.split()] + sentence1_words = [word for word in sentence1.split()] + tokens1 = [Token(word) for word in sentence1_words] sentence2 = "the quick brown fox jumped over the laziest lazy elmo" - tokens2 = [Token(word) for word in sentence2.split()] + sentence2_words = [word for word in sentence2.split()] + tokens2 = [Token(word) for word in sentence2_words] instance1 = Instance( - {"tokens": TextField(tokens1, {"bert": token_indexer})} + {"tokens": TextField(tokens1, {"bert": token_indexer}), + "metadata": MetadataField({"words": sentence1_words})} ) instance2 = Instance( - {"tokens": TextField(tokens2, {"bert": token_indexer})} + {"tokens": TextField(tokens2, {"bert": token_indexer}), + "metadata": MetadataField({"words": sentence2_words})} ) self.batch = Batch([instance1, instance2]) @@ -71,7 +78,7 @@ def test_forward_pass_runs_correctly(self): self.batch.index_instances(self.vocab) padding_lengths = self.batch.get_padding_lengths() training_tensors = self.batch.as_tensor_dict(padding_lengths) - + training_tensors = move_to_device(training_tensors, self.device_index) model = Seq2Labels( vocab=self.vocab, text_field_embedder=self.text_field_embedder, @@ -91,8 +98,10 @@ def test_forward_pass_runs_correctly(self): "max_error_probability", "labels", "d_tags", + "words", + "corrected_words" ] ) - probs = output_dict["class_probabilities_labels"][0].data.numpy() + probs = output_dict["class_probabilities_labels"][0].data.cpu().numpy() np.testing.assert_almost_equal(np.sum(probs, -1), np.full((10), 1), 5)