def __init__(self, args, logger=None) -> None:
self.args = args
self.logger = logger
# Load NER model
self.ner_model = NER.get_model()
# load biencoder model
if logger:
logger.info("loading biencoder model")
with open(args.biencoder_config) as json_file:
biencoder_params = json.load(json_file)
biencoder_params["path_to_model"] = args.biencoder_model
biencoder = load_biencoder(biencoder_params)
crossencoder = None
crossencoder_params = None
if not args.fast:
# load crossencoder model
if logger:
logger.info("loading crossencoder model")
with open(args.crossencoder_config) as json_file:
crossencoder_params = json.load(json_file)
crossencoder_params["path_to_model"] = args.crossencoder_model
crossencoder = load_crossencoder(crossencoder_params)
# load candidate entities
if logger:
logger.info("loading candidate entities")
(
candidate_encoding,
title2id,
id2title,
id2text,
wikipedia_id2local_id,
faiss_indexer,
) = _load_candidates(
args.entity_catalogue,
args.entity_encoding,
faiss_index=args.faiss_index,
index_path=args.index_path,
logger=logger,
)
self.biencoder = biencoder
self.biencoder_params = biencoder_params
self.crossencoder = crossencoder
self.crossencoder_params = crossencoder_params
self.candidate_encoding = candidate_encoding
self.title2id = title2id
self.id2title = id2title
self.id2text = id2text
self.wikipedia_id2local_id = wikipedia_id2local_id
self.faiss_indexer = faiss_indexer
self.id2url = {
v: "https://en.wikipedia.org/wiki?curid=%s" % k
for k, v in wikipedia_id2local_id.items()
}
def run(
args,
logger,
biencoder,
biencoder_params,
crossencoder,
crossencoder_params,
candidate_encoding,
title2id,
id2title,
id2text,
wikipedia_id2local_id,
test_data=None,
):
if not test_data and not args.test_mentions and not args.interactive:
msg = (
"ERROR: either you start BLINK with the "
"interactive option (-i) or you pass in input test mentions (--test_mentions)"
"and test entitied (--test_entities)"
)
raise ValueError(msg)
id2url = {
v : 'https://en.wikipedia.org/wiki?curid=%s' % k
for k, v in wikipedia_id2local_id.items()
}
stopping_condition = False
while not stopping_condition:
samples = None
if args.interactive:
logger.info("interactive mode")
# biencoder_params["eval_batch_size"] = 1
# Load NER model
ner_model = NER.get_model()
# Interactive
text = input("insert text:")
# Identify mentions
samples = _annotate(ner_model, [text])
_print_colorful_text(text, samples)
else:
logger.info("test dataset mode")
if test_data:
samples = test_data
else:
# Load test mentions
samples = _get_test_samples(
args.test_mentions,
args.test_entities,
title2id,
wikipedia_id2local_id,
logger,
)
stopping_condition = True
# don't look at labels
keep_all = (
args.interactive
or samples[0]["label"] == "unknown"
or samples[0]["label_id"] < 0
)
# prepare the data for biencoder
logger.info("preparing data for biencoder")
dataloader = _process_biencoder_dataloader(
samples, biencoder.tokenizer, biencoder_params
)
# run biencoder
logger.info("run biencoder")
top_k = args.top_k
labels, nns, scores = _run_biencoder(
biencoder, dataloader, candidate_encoding, top_k
)
if args.interactive:
print("\nfast (biencoder) predictions:")
_print_colorful_text(text, samples)
# print biencoder prediction
idx = 0
for entity_list, sample in zip(nns, samples):
e_id = entity_list[0]
e_title = id2title[e_id]
e_text = id2text[e_id]
e_url = id2url[e_id]
_print_colorful_prediction(idx, sample, e_id, e_title, e_text, e_url, args.show_url)
idx += 1
print()
if args.fast:
# use only biencoder
continue
else:
biencoder_accuracy = -1
recall_at = -1
if not keep_all:
# get recall values
top_k = args.top_k
x = []
y = []
for i in range(1, top_k):
temp_y = 0.0
for label, top in zip(labels, nns):
if label in top[:i]:
temp_y += 1
if len(labels) > 0:
temp_y /= len(labels)
x.append(i)
y.append(temp_y)
# plt.plot(x, y)
biencoder_accuracy = y[0]
recall_at = y[-1]
print("biencoder accuracy: %.4f" % biencoder_accuracy)
print("biencoder recall@%d: %.4f" % (top_k, y[-1]))
if args.fast:
predictions = []
for entity_list in nns:
sample_prediction = []
for e_id in entity_list:
e_title = id2title[e_id]
sample_prediction.append(e_title)
predictions.append(sample_prediction)
# use only biencoder
return (
biencoder_accuracy,
recall_at,
-1,
-1,
len(samples),
predictions,
scores,
)
# prepare crossencoder data
context_input, candidate_input, label_input = prepare_crossencoder_data(
crossencoder.tokenizer, samples, labels, nns, id2title, id2text, keep_all,
)
context_input = modify(
context_input, candidate_input, crossencoder_params["max_seq_length"]
)
dataloader = _process_crossencoder_dataloader(
context_input, label_input, crossencoder_params
)
# run crossencoder and get accuracy
accuracy, index_array, unsorted_scores = _run_crossencoder(
crossencoder,
dataloader,
logger,
context_len=biencoder_params["max_context_length"],
)
if args.interactive:
print("\naccurate (crossencoder) predictions:")
_print_colorful_text(text, samples)
# print crossencoder prediction
idx = 0
for entity_list, index_list, sample in zip(nns, index_array, samples):
e_id = entity_list[index_list[-1]]
e_title = id2title[e_id]
e_text = id2text[e_id]
e_url = id2url[e_id]
_print_colorful_prediction(idx, sample, e_id, e_title, e_text, e_url, args.show_url)
idx += 1
print()
else:
scores = []
predictions = []
for entity_list, index_list, scores_list in zip(
nns, index_array, unsorted_scores
):
index_list = index_list.tolist()
# descending order
index_list.reverse()
sample_prediction = []
sample_scores = []
for index in index_list:
e_id = entity_list[index]
e_title = id2title[e_id]
sample_prediction.append(e_title)
sample_scores.append(scores_list[index])
predictions.append(sample_prediction)
scores.append(sample_scores)
crossencoder_normalized_accuracy = -1
overall_unormalized_accuracy = -1
if not keep_all:
crossencoder_normalized_accuracy = accuracy
print(
"crossencoder normalized accuracy: %.4f"
% crossencoder_normalized_accuracy
)
overall_unormalized_accuracy = (
crossencoder_normalized_accuracy * len(label_input) / len(samples)
)
print(
"overall unnormalized accuracy: %.4f" % overall_unormalized_accuracy
)
return (
biencoder_accuracy,
recall_at,
crossencoder_normalized_accuracy,
overall_unormalized_accuracy,
len(samples),
predictions,
scores,
)
def load_model(self):
#self.biencoder, self.biencoder_params, self.candidate_encoding, self.faiss_indexer \
# = load_models(logger=self.logger)
self.ner_model = NER.get_model()
entity_catalogue,
entity_encoding,
faiss_index=None,
index_path=None,
logger=logger,
)
return biencoder, biencoder_params, candidate_encoding, faiss_indexer
top_k = 1
logger = utils.get_logger('output')
biencoder, biencoder_params, candidate_encoding, faiss_indexer = load_models(
logger=logger)
ner_model = NER.get_model()
samples = _annotate(ner_model, [
'What is throat cancer? Throat cancer is any cancer that forms in the throat. The throat, also called the pharynx, is a 5-inch-long tube that runs from your nose to your neck. The larynx (voice box) and pharynx are the two main places throat cancer forms. Throat cancer is a type of head and neck cancer, which includes cancer of the mouth, tonsils, nose, sinuses, salivary glands and neck lymph nodes.',
"Juan Carlos is the king os Spain", "Cristiano Ronaldo has 5 Ballon D'Or"
])
print(samples)
dataloader = _process_biencoder_dataloader(samples, biencoder.tokenizer,
biencoder_params)
labels, nns, scores = _run_biencoder(biencoder, dataloader, candidate_encoding,
top_k, faiss_indexer)
print(labels, nns, scores)
logger.info(nns)
logger.info(scores)
def main(parameters):
print("Parameters:", parameters)
# Read data
sentences = utils.read_sentences_from_file(
parameters["path_to_input_file"],
one_sentence_per_line=parameters["one_sentence_per_line"],
)
# Identify mentions
ner_model = NER.get_model(parameters)
ner_output_data = ner_model.predict(sentences)
sentences = ner_output_data["sentences"]
mentions = ner_output_data["mentions"]
output_folder_path = parameters["output_folder_path"]
if ((output_folder_path is not None) and os.path.exists(output_folder_path)
and os.listdir(output_folder_path)):
print(
"The given output directory ({}) already exists and is not empty.".
format(output_folder_path))
answer = input(
"Would you like to empty the existing directory? [Y/N]\n")
if answer.strip() == "Y":
print("Deleting {}...".format(output_folder_path))
shutil.rmtree(output_folder_path)
else:
raise ValueError(
"Output directory ({}) already exists and is not empty.".
format(output_folder_path))
if output_folder_path is not None:
utils.write_dicts_as_json_per_line(
sentences, utils.get_sentences_txt_file_path(output_folder_path))
utils.write_dicts_as_json_per_line(
mentions, utils.get_mentions_txt_file_path(output_folder_path))
# Generate candidates and get the data that describes the candidates
candidate_generator = CG.get_model(parameters)
candidate_generator.process_mentions_for_candidate_generator(
sentences=sentences, mentions=mentions)
for mention in mentions:
mention["candidates"] = candidate_generator.get_candidates(mention)
if parameters["consider_additional_datafetcher"]:
data_fetcher = CDF.get_model(parameters)
for candidate in mention["candidates"]:
data_fetcher.get_data_for_entity(candidate)
if output_folder_path is not None:
utils.write_dicts_as_json_per_line(
mentions, utils.get_mentions_txt_file_path(output_folder_path))
# Reranking
reranking_model = R.get_model(parameters)
reranking_model.rerank(mentions, sentences)
if output_folder_path is not None:
utils.write_dicts_as_json_per_line(
mentions, utils.get_mentions_txt_file_path(output_folder_path))
utils.write_end2end_pickle_output(sentences, mentions,
output_folder_path)
utils.present_annotated_sentences(
sentences,
mentions,
utils.get_end2end_pretty_output_file_path(output_folder_path),
)
# Showcase results
utils.present_annotated_sentences(sentences, mentions)
