def main():
parser = argparse.ArgumentParser()
parser.add_argument("model")
args = parser.parse_args()
model_dir = ModelDir(args.model)
checkpoint = model_dir.get_best_weights()
reader = tf.train.NewCheckpointReader(checkpoint)
if reader.has_tensor("weight_embed_context_lm/layer_0/w"):
x = "w"
else:
x = "ELMo_W_0"
for i in reader.get_variable_to_shape_map().items():
print(i)
input_w = reader.get_tensor(
"weight_embed_lm/layer_0/%s/ExponentialMovingAverage" % x)
output_w = reader.get_tensor(
"weight_lm/layer_0/%s/ExponentialMovingAverage" % x)
print("Input")
print(input_w)
print("(Softmax): " + str(softmax(input_w)))
print("Output")
print(output_w)
print("(Softmax): " + str(softmax(output_w)))
def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument("model")
args = parser.parse_args()
model_dir = ModelDir(args.model)
checkpoint = model_dir.get_best_weights()
print(checkpoint)
if checkpoint is None:
print("Show latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
else:
print("Show best weights")
reader = tf.train.NewCheckpointReader(checkpoint)
param_map = reader.get_variable_to_shape_map()
total = 0
for k in sorted(param_map):
v = param_map[k]
print('%s: %s' % (k, str(v)))
total += np.prod(v)
print("%d total" % total)
def main():
parser = argparse.ArgumentParser(description='Evaluate a model on TriviaQA data')
parser.add_argument('model', help='model directory')
parser.add_argument('-p', '--paragraph_output', type=str,
help="Save fine grained results for each paragraph in csv format")
parser.add_argument('-o', '--official_output', type=str, help="Build an offical output file with the model's"
" most confident span for each (question, doc) pair")
parser.add_argument('--no_ema', action="store_true", help="Don't use EMA weights even if they exist")
parser.add_argument('--n_processes', type=int, default=None,
help="Number of processes to do the preprocessing (selecting paragraphs+loading context) with")
parser.add_argument('-i', '--step', type=int, default=None, help="checkpoint to load, default to latest")
parser.add_argument('-n', '--n_sample', type=int, default=None, help="Number of questions to evaluate on")
parser.add_argument('-a', '--async', type=int, default=10)
parser.add_argument('-t', '--tokens', type=int, default=400,
help="Max tokens per a paragraph")
parser.add_argument('-g', '--n_paragraphs', type=int, default=15,
help="Number of paragraphs to run the model on")
parser.add_argument('-f', '--filter', type=str, default=None, choices=["tfidf", "truncate", "linear"],
help="How to select paragraphs")
parser.add_argument('-b', '--batch_size', type=int, default=200,
help="Batch size, larger sizes might be faster but wll take more memory")
parser.add_argument('--max_answer_len', type=int, default=8,
help="Max answer span to select")
parser.add_argument('-c', '--corpus',
choices=["web-dev", "web-test", "web-verified-dev", "web-train",
"open-dev", "open-train"],
default="web-verified-dev")
args = parser.parse_args()
model_dir = ModelDir(args.model)
model = model_dir.get_model()
if args.corpus.startswith('web'):
dataset = TriviaQaWebDataset()
corpus = dataset.evidence
if args.corpus == "web-dev":
test_questions = dataset.get_dev()
elif args.corpus == "web-test":
test_questions = dataset.get_test()
elif args.corpus == "web-verified-dev":
test_questions = dataset.get_verified()
elif args.corpus == "web-train":
test_questions = dataset.get_train()
else:
raise RuntimeError()
else:
dataset = TriviaQaOpenDataset()
corpus = dataset.evidence
if args.corpus == "open-dev":
test_questions = dataset.get_dev()
elif args.corpus == "open-train":
test_questions = dataset.get_train()
else:
raise RuntimeError()
splitter = MergeParagraphs(args.tokens)
per_document = not args.corpus.startswith("open")
filter_name = args.filter
if filter_name is None:
if args.corpus.startswith("open"):
filter_name = "linear"
else:
filter_name = "tfidf"
print("Selecting %d paragraphs using %s method per %s" % (args.n_paragraphs, filter_name,
("question-document pair" if per_document else "question")))
if filter_name == "tfidf":
para_filter = TopTfIdf(NltkPlusStopWords(punctuation=True), args.n_paragraphs)
elif filter_name == "truncate":
para_filter = FirstN(args.n_paragraphs)
elif filter_name == "linear":
para_filter = ShallowOpenWebRanker(args.n_paragraphs)
else:
raise ValueError()
n_questions = args.n_sample
if n_questions is not None:
test_questions.sort(key=lambda x:x.question_id)
np.random.RandomState(0).shuffle(test_questions)
test_questions = test_questions[:n_questions]
print("Building question/paragraph pairs...")
# Loads the relevant questions/documents, selects the right paragraphs, and runs the model's preprocessor
if per_document:
prep = ExtractMultiParagraphs(splitter, para_filter, model.preprocessor, require_an_answer=False)
else:
prep = ExtractMultiParagraphsPerQuestion(splitter, para_filter, model.preprocessor, require_an_answer=False)
prepped_data = preprocess_par(test_questions, corpus, prep, args.n_processes, 1000)
data = []
for q in prepped_data.data:
for i, p in enumerate(q.paragraphs):
if q.answer_text is None:
ans = None
else:
ans = TokenSpans(q.answer_text, p.answer_spans)
data.append(DocumentParagraphQuestion(q.question_id, p.doc_id,
(p.start, p.end), q.question, p.text,
ans, i))
# Reverse so our first batch will be the largest (so OOMs happen early)
questions = sorted(data, key=lambda x: (x.n_context_words, len(x.question)), reverse=True)
print("Done, starting eval")
if args.step is not None:
if args.step == "latest":
checkpoint = model_dir.get_latest_checkpoint()
else:
checkpoint = model_dir.get_checkpoint(int(args.step))
else:
checkpoint = model_dir.get_best_weights()
if checkpoint is not None:
print("Using best weights")
else:
print("Using latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
test_questions = ParagraphAndQuestionDataset(questions, FixedOrderBatcher(args.batch_size, True))
evaluation = trainer.test(model,
[RecordParagraphSpanPrediction(args.max_answer_len, True)],
{args.corpus:test_questions}, ResourceLoader(), checkpoint, not args.no_ema, args.async)[args.corpus]
if not all(len(x) == len(data) for x in evaluation.per_sample.values()):
raise RuntimeError()
df = pd.DataFrame(evaluation.per_sample)
if args.official_output is not None:
print("Saving question result")
# I didn't store the unormalized filenames exactly, so unfortunately we have to reload
# the source data to get exact filename to output an official test script
fns = {}
print("Loading proper filenames")
if args.corpus == 'web-test':
source = join(TRIVIA_QA, "qa", "web-test-without-answers.json")
elif args.corpus == "web-dev":
source = join(TRIVIA_QA, "qa", "web-dev.json")
else:
raise NotImplementedError()
with open(join(source)) as f:
data = json.load(f)["Data"]
for point in data:
for doc in point["EntityPages"]:
filename = doc["Filename"]
fn = join("wikipedia", filename[:filename.rfind(".")])
fn = normalize_wiki_filename(fn)
fns[(point["QuestionId"], fn)] = filename
answers = {}
scores = {}
for q_id, doc_id, start, end, txt, score in df[["question_id", "doc_id", "para_start", "para_end",
"text_answer", "predicted_score"]].itertuples(index=False):
filename = dataset.evidence.file_id_map[doc_id]
if filename.startswith("web"):
true_name = filename[4:] + ".txt"
else:
true_name = fns[(q_id, filename)]
key = q_id + "--" + true_name
prev_score = scores.get(key)
if prev_score is None or prev_score < score:
scores[key] = score
answers[key] = txt
with open(args.official_output, "w") as f:
json.dump(answers, f)
if per_document:
group_by = ["question_id", "doc_id"]
else:
group_by = ["question_id"]
# Print a table of scores as more paragraphs are used
df.sort_values(group_by + ["rank"], inplace=True)
f1 = compute_model_scores(df, "predicted_score", "text_f1", group_by)
em = compute_model_scores(df, "predicted_score", "text_em", group_by)
table = [["N Paragraphs", "EM", "F1"]]
table += list([str(i+1), "%.4f" % e, "%.4f" % f] for i, (e, f) in enumerate(zip(em, f1)))
print_table(table)
output_file = args.paragraph_output
if output_file is not None:
print("Saving paragraph result")
if output_file.endswith("json"):
with open(output_file, "w") as f:
json.dump(evaluation.per_sample, f)
elif output_file.endswith("pkl"):
with open(output_file, "wb") as f:
pickle.dump(evaluation.per_sample, f)
elif output_file.endswith("csv"):
df.to_csv(output_file, index=False)
else:
raise ValueError("Unrecognized file format")
def main():
parser = argparse.ArgumentParser(description='Evaluate a model on SQuAD')
parser.add_argument('model', help='model directory to evaluate')
parser.add_argument("-o", "--official_output", type=str,
help="where to output an official result file")
parser.add_argument('-n', '--sample_questions', type=int, default=None,
help="(for testing) run on a subset of questions")
parser.add_argument('--answer_bounds', nargs='+', type=int, default=[17],
help="Max size of answer")
parser.add_argument('-b', '--batch_size', type=int, default=200,
help="Batch size, larger sizes can be faster but uses more memory")
parser.add_argument('-s', '--step', default=None,
help="Weights to load, can be a checkpoint step or 'latest'")
# Add ja_test choice to test Multilingual QA dataset.
parser.add_argument(
'-c', '--corpus', choices=["dev", "train", "ja_test", "pred"], default="dev")
parser.add_argument('--no_ema', action="store_true",
help="Don't use EMA weights even if they exist")
# Add ja_test choice to test Multilingual QA pipeline.
parser.add_argument('-p', '--pred_filepath', default=None,
help="The csv file path if you try pred mode")
args = parser.parse_args()
model_dir = ModelDir(args.model)
corpus = SquadCorpus()
if args.corpus == "dev":
questions = corpus.get_dev()
# Add ja_test choice to test Multilingual QA pipeline.
elif args.corpus == "ja_test":
questions = corpus.get_ja_test()
# This is for prediction mode for MLQA pipeline.
elif args.corpus == "pred":
questions = create_pred_dataset(args.pred_filepath)
else:
questions = corpus.get_train()
questions = split_docs(questions)
if args.sample_questions:
np.random.RandomState(0).shuffle(
sorted(questions, key=lambda x: x.question_id))
questions = questions[:args.sample_questions]
questions.sort(key=lambda x: x.n_context_words, reverse=True)
dataset = ParagraphAndQuestionDataset(
questions, FixedOrderBatcher(args.batch_size, True))
evaluators = [SpanEvaluator(args.answer_bounds, text_eval="squad")]
if args.official_output is not None:
evaluators.append(RecordSpanPrediction(args.answer_bounds[0]))
if args.step is not None:
if args.step == "latest":
checkpoint = model_dir.get_latest_checkpoint()
else:
checkpoint = model_dir.get_checkpoint(int(args.step))
else:
checkpoint = model_dir.get_best_weights()
if checkpoint is not None:
print("Using best weights")
else:
print("Using latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
model = model_dir.get_model()
evaluation = trainer.test(model, evaluators, {args.corpus: dataset},
corpus.get_resource_loader(), checkpoint, not args.no_ema)[args.corpus]
# Print the scalar results in a two column table
scalars = evaluation.scalars
cols = list(sorted(scalars.keys()))
table = [cols]
header = ["Metric", ""]
table.append([("%s" % scalars[x] if x in scalars else "-") for x in cols])
print_table([header] + transpose_lists(table))
# Save the official output
if args.official_output is not None:
quid_to_para = {}
for x in questions:
quid_to_para[x.question_id] = x.paragraph
q_id_to_answers = {}
q_ids = evaluation.per_sample["question_id"]
spans = evaluation.per_sample["predicted_span"]
for q_id, (start, end) in zip(q_ids, spans):
text = quid_to_para[q_id].get_original_text(start, end)
q_id_to_answers[q_id] = text
with open(args.official_output, "w") as f:
json.dump(q_id_to_answers, f)
def run():
parser = argparse.ArgumentParser()
parser.add_argument("squad_path", help="path to squad dev data file")
parser.add_argument("output_path",
help="path where evaluation json file will be written")
parser.add_argument("--model-path",
default="model",
help="path to model directory")
parser.add_argument("--n", type=int, default=None)
parser.add_argument("-b", "--batch_size", type=int, default=100)
parser.add_argument("--ema", action="store_true")
args = parser.parse_args()
squad_path = args.squad_path
output_path = args.output_path
model_dir = ModelDir(args.model_path)
nltk.data.path.append("nltk_data")
print("Loading data")
docs = parse_squad_data(squad_path, "", NltkAndPunctTokenizer(), False)
pairs = split_docs(docs)
dataset = ParagraphAndQuestionDataset(
pairs, ClusteredBatcher(args.batch_size, ContextLenKey(), False, True))
print("Done, init model")
model = model_dir.get_model()
loader = ResourceLoader(lambda a, b: load_word_vector_file(
join(VEC_DIR, "glove.840B.300d.txt"), b))
lm_model = model.lm_model
basedir = join(LM_DIR, "squad-context-concat-skip")
lm_model.lm_vocab_file = join(basedir,
"squad_train_dev_all_unique_tokens.txt")
lm_model.options_file = join(
basedir, "options_squad_lm_2x4096_512_2048cnn_2xhighway_skip.json")
lm_model.weight_file = join(
basedir,
"squad_context_concat_lm_2x4096_512_2048cnn_2xhighway_skip.hdf5")
lm_model.embed_weights_file = None
model.set_inputs([dataset], loader)
print("Done, building graph")
sess = tf.Session()
with sess.as_default():
pred = model.get_prediction()
best_span = pred.get_best_span(17)[0]
all_vars = tf.global_variables() + tf.get_collection(
tf.GraphKeys.SAVEABLE_OBJECTS)
dont_restore_names = {
x.name
for x in all_vars if x.name.startswith("bilm")
}
print(sorted(dont_restore_names))
vars = [x for x in all_vars if x.name not in dont_restore_names]
print("Done, loading weights")
checkpoint = model_dir.get_best_weights()
if checkpoint is None:
print("Loading most recent checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
else:
print("Loading best weights")
saver = tf.train.Saver(vars)
saver.restore(sess, checkpoint)
if args.ema:
ema = tf.train.ExponentialMovingAverage(0)
saver = tf.train.Saver(
{ema.average_name(x): x
for x in tf.trainable_variables()})
saver.restore(sess, checkpoint)
sess.run(
tf.variables_initializer(
[x for x in all_vars if x.name in dont_restore_names]))
print("Done, starting evaluation")
out = {}
for i, batch in enumerate(dataset.get_epoch()):
if args.n is not None and i == args.n:
break
print("On batch: %d" % (i + 1))
enc = model.encode(batch, False)
spans = sess.run(best_span, feed_dict=enc)
for (s, e), point in zip(spans, batch):
out[point.question_id] = point.get_original_text(s, e)
sess.close()
print("Done, saving")
with open(output_path, "w") as f:
json.dump(out, f)
print("Mission accomplished!")
def main():
parser = argparse.ArgumentParser(description='Evaluate a model on SQuAD')
parser.add_argument('model', help='model directory to evaluate')
parser.add_argument("-o", "--official_output", type=str, help="where to output an official result file")
parser.add_argument('-n', '--sample_questions', type=int, default=None,
help="(for testing) run on a subset of questions")
parser.add_argument('--answer_bounds', nargs='+', type=int, default=[17],
help="Max size of answer")
parser.add_argument('-b', '--batch_size', type=int, default=200,
help="Batch size, larger sizes can be faster but uses more memory")
parser.add_argument('-s', '--step', default=None,
help="Weights to load, can be a checkpoint step or 'latest'")
parser.add_argument('-c', '--corpus', choices=["dev", "train"], default="dev")
parser.add_argument('--no_ema', action="store_true", help="Don't use EMA weights even if they exist")
parser.add_argument('--none_prob', action="store_true", help="Output none probability for samples")
parser.add_argument('--elmo', action="store_true", help="Use elmo model")
parser.add_argument('--per_question_loss_file', type=str, default=None,
help="Run question by question and output a question_id -> loss output to this file")
args = parser.parse_known_args()[0]
model_dir = ModelDir(args.model)
corpus = SquadCorpus()
if args.corpus == "dev":
questions = corpus.get_dev()
else:
questions = corpus.get_train()
questions = split_docs(questions)
if args.sample_questions:
np.random.RandomState(0).shuffle(sorted(questions, key=lambda x: x.question_id))
questions = questions[:args.sample_questions]
questions.sort(key=lambda x:x.n_context_words, reverse=True)
dataset = ParagraphAndQuestionDataset(questions, FixedOrderBatcher(args.batch_size, True))
evaluators = [SpanEvaluator(args.answer_bounds, text_eval="squad")]
if args.official_output is not None:
evaluators.append(RecordSpanPrediction(args.answer_bounds[0]))
if args.per_question_loss_file is not None:
evaluators.append(RecordSpanPredictionScore(args.answer_bounds[0], args.batch_size, args.none_prob))
if args.step is not None:
if args.step == "latest":
checkpoint = model_dir.get_latest_checkpoint()
else:
checkpoint = model_dir.get_checkpoint(int(args.step))
else:
checkpoint = model_dir.get_best_weights()
if checkpoint is not None:
print("Using best weights")
else:
print("Using latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
model = model_dir.get_model()
if args.elmo:
model.lm_model.lm_vocab_file = './elmo-params/squad_train_dev_all_unique_tokens.txt'
model.lm_model.options_file = './elmo-params/options_squad_lm_2x4096_512_2048cnn_2xhighway_skip.json'
model.lm_model.weight_file = './elmo-params/squad_context_concat_lm_2x4096_512_2048cnn_2xhighway_skip.hdf5'
model.lm_model.embed_weights_file = None
evaluation = trainer.test(model, evaluators, {args.corpus: dataset},
corpus.get_resource_loader(), checkpoint, not args.no_ema)[args.corpus]
# Print the scalar results in a two column table
scalars = evaluation.scalars
cols = list(sorted(scalars.keys()))
table = [cols]
header = ["Metric", ""]
table.append([("%s" % scalars[x] if x in scalars else "-") for x in cols])
print_table([header] + transpose_lists(table))
# Save the official output
if args.official_output is not None:
quid_to_para = {}
for x in questions:
quid_to_para[x.question_id] = x.paragraph
q_id_to_answers = {}
q_ids = evaluation.per_sample["question_id"]
spans = evaluation.per_sample["predicted_span"]
for q_id, (start, end) in zip(q_ids, spans):
text = quid_to_para[q_id].get_original_text(start, end)
q_id_to_answers[q_id] = text
with open(args.official_output, "w") as f:
json.dump(q_id_to_answers, f)
if args.per_question_loss_file is not None:
print("Saving result")
output_file = args.per_question_loss_file
ids = evaluation.per_sample["question_ids"]
f1s = evaluation.per_sample["text_f1"]
ems = evaluation.per_sample["text_em"]
losses = evaluation.per_sample["loss"]
if args.none_prob:
none_probs = evaluation.per_sample["none_probs"]
"""
results = {question_id: {'f1': float(f1), 'em': float(em), 'loss': float(loss), 'none_prob': float(none_prob)} for question_id, f1, em, loss, none_prob in zip(ids, f1s, ems, losses, none_probs)}
"""
results = {question_id: float(none_prob) for question_id, none_prob in zip(ids, none_probs)}
else:
results = {question_id: {'f1': float(f1), 'em': float(em), 'loss': float(loss)} for question_id, f1, em, loss in zip(ids, f1s, ems, losses)}
with open(output_file, 'w') as f:
json.dump(results, f)
def main():
parser = argparse.ArgumentParser(
description='Evaluate a model on document-level SQuAD')
parser.add_argument('model', help='model to use')
parser.add_argument(
'output',
type=str,
help="Store the per-paragraph results in csv format in this file")
parser.add_argument('-n',
'--n_sample',
type=int,
default=None,
help="(for testing) sample documents")
parser.add_argument(
'-s',
'--async',
type=int,
default=10,
help="Encoding batch asynchronously, queueing up to this many")
parser.add_argument('-a',
'--answer_bound',
type=int,
default=17,
help="Max answer span length")
parser.add_argument('-p',
'--n_paragraphs',
type=int,
default=None,
help="Max number of paragraphs to use")
parser.add_argument(
'-b',
'--batch_size',
type=int,
default=200,
help="Batch size, larger sizes can be faster but uses more memory")
parser.add_argument('-c',
'--corpus',
choices=["dev", "train", "doc-rd-dev"],
default="dev")
parser.add_argument('--no_ema',
action="store_true",
help="Don't use EMA weights even if they exist")
args = parser.parse_args()
model_dir = ModelDir(args.model)
print("Loading data")
questions = []
ranker = SquadTfIdfRanker(NltkPlusStopWords(True),
args.n_paragraphs,
force_answer=False)
if args.corpus == "doc-rd-dev":
docs = SquadCorpus().get_dev()
if args.n_sample is not None:
docs.sort(key=lambda x: x.doc_id)
np.random.RandomState(0).shuffle(docs)
docs = docs[:args.n_sample]
print("Fetching document reader docs...")
doc_rd_versions = get_doc_rd_doc(docs)
print("Ranking and matching with questions...")
for doc in tqdm(docs):
doc_questions = flatten_iterable(x.questions
for x in doc.paragraphs)
paragraphs = doc_rd_versions[doc.title]
ranks = ranker.rank([x.words for x in doc_questions],
[x.text for x in paragraphs])
for i, question in enumerate(doc_questions):
para_ranks = np.argsort(ranks[i])
for para_rank, para_num in enumerate(
para_ranks[:args.n_paragraphs]):
# Just use dummy answers spans for these pairs
questions.append(
RankedParagraphQuestion(
question.words,
TokenSpans(question.answer.answer_text,
np.zeros((0, 2), dtype=np.int32)),
question.question_id, paragraphs[para_num],
para_rank, para_num))
rl = ResourceLoader()
else:
if args.corpus == "dev":
docs = SquadCorpus().get_dev()
else:
docs = SquadCorpus().get_train()
rl = SquadCorpus().get_resource_loader()
if args.n_sample is not None:
docs.sort(key=lambda x: x.doc_id)
np.random.RandomState(0).shuffle(docs)
docs = docs[:args.n_sample]
for q in ranker.ranked_questions(docs):
for i, p in enumerate(q.paragraphs):
questions.append(
RankedParagraphQuestion(
q.question, TokenSpans(q.answer_text, p.answer_spans),
q.question_id,
ParagraphWithInverse([p.text], p.original_text,
p.spans), i, p.paragraph_num))
print("Split %d docs into %d paragraphs" % (len(docs), len(questions)))
questions = sorted(questions,
key=lambda x: (x.n_context_words, len(x.question)),
reverse=True)
for q in questions:
if len(q.answer.answer_spans.shape) != 2:
raise ValueError()
checkpoint = model_dir.get_best_weights()
if checkpoint is not None:
print("Using best weights")
else:
print("Using latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
if checkpoint is None:
raise ValueError("No checkpoints found")
data = ParagraphAndQuestionDataset(
questions, FixedOrderBatcher(args.batch_size, True))
model = model_dir.get_model()
evaluation = trainer.test(
model, [RecordParagraphSpanPrediction(args.answer_bound, True)],
{args.corpus: data}, rl, checkpoint, not args.no_ema,
args. async)[args.corpus]
print("Saving result")
output_file = args.output
df = pd.DataFrame(evaluation.per_sample)
df.sort_values(["question_id", "rank"], inplace=True, ascending=True)
group_by = ["question_id"]
f1 = compute_ranked_scores(df, "predicted_score", "text_f1", group_by)
em = compute_ranked_scores(df, "predicted_score", "text_em", group_by)
table = [["N Paragraphs", "EM", "F1"]]
table += list([str(i + 1), "%.4f" % e, "%.4f" % f]
for i, (e, f) in enumerate(zip(em, f1)))
print_table(table)
df.to_csv(output_file, index=False)
def main():
parser = argparse.ArgumentParser(
description='Train a model on document-level SQuAD')
parser.add_argument(
'mode',
choices=["paragraph", "confidence", "shared-norm", "merge", "sigmoid"])
parser.add_argument("name", help="Output directory")
args = parser.parse_args()
mode = args.mode
out = args.name + "-" + datetime.now().strftime("%m%d-%H%M%S")
corpus = SquadCorpus()
if mode == "merge":
# Adds paragraph start tokens, since we will be concatenating paragraphs together
pre = WithIndicators(True, para_tokens=False, doc_start_token=False)
else:
pre = None
# model = get_model(50, 100, args.mode, pre)
tmp = ModelDir("models/squad-shared-norm")
model = tmp.get_model()
if mode == "paragraph":
# Run in the "standard" known-paragraph setting
if model.preprocessor is not None:
raise NotImplementedError()
n_epochs = 26
train_batching = ClusteredBatcher(45, ContextLenBucketedKey(3), True,
False)
eval_batching = ClusteredBatcher(45, ContextLenKey(), False, False)
data = DocumentQaTrainingData(corpus, None, train_batching,
eval_batching)
eval = [LossEvaluator(), SpanEvaluator(bound=[17], text_eval="squad")]
else:
eval_set_mode = {
"confidence": "flatten",
"sigmoid": "flatten",
"shared-norm": "group",
"merge": "merge"
}[mode]
eval_dataset = RandomParagraphSetDatasetBuilder(
100, eval_set_mode, True, 0)
if mode == "confidence" or mode == "sigmoid":
if mode == "sigmoid":
# needs to be trained for a really long time for reasons unknown, even this might be too small
n_epochs = 100
else:
n_epochs = 50 # more epochs since we only "see" the label very other epoch-osh
train_batching = ClusteredBatcher(45, ContextLenBucketedKey(3),
True, False)
data = PreprocessedData(
SquadCorpus(),
SquadTfIdfRanker(NltkPlusStopWords(True), 4, True,
model.preprocessor),
StratifyParagraphsBuilder(train_batching, 1),
eval_dataset,
eval_on_verified=False,
)
else:
n_epochs = 26
data = PreprocessedData(
SquadCorpus(),
SquadTfIdfRanker(NltkPlusStopWords(True), 4, True,
model.preprocessor),
StratifyParagraphSetsBuilder(25, args.mode == "merge", True,
1),
eval_dataset,
eval_on_verified=False,
)
eval = [LossEvaluator(), MultiParagraphSpanEvaluator(17, "squad")]
data.preprocess(1)
with open(__file__, "r") as f:
notes = f.read()
notes = args.mode + "\n" + notes
params = train_params(n_epochs)
if mode == "paragraph":
params.best_weights = ("dev", "b17/text-f1")
trainer.start_training(data,
model,
params,
eval,
model_dir.ModelDir(out),
notes,
initialize_from=tmp.get_best_weights())
def main():
parser = argparse.ArgumentParser(
description='Evaluate a model on TriviaQA data')
parser.add_argument('model', help='model directory')
parser.add_argument(
'-p',
'--paragraph_output',
type=str,
help="Save fine grained results for each paragraph in csv format")
parser.add_argument('-o',
'--official_output',
type=str,
help="Build an offical output file with the model's"
" most confident span for each (question, doc) pair")
parser.add_argument('--no_ema',
action="store_true",
help="Don't use EMA weights even if they exist")
parser.add_argument(
'--n_processes',
type=int,
default=None,
help=
"Number of processes to do the preprocessing (selecting paragraphs+loading context) with"
)
parser.add_argument('-i',
'--step',
type=int,
default=None,
help="checkpoint to load, default to latest")
parser.add_argument('-n',
'--n_sample',
type=int,
default=None,
help="Number of questions to evaluate on")
parser.add_argument('-a', '--async', type=int, default=10)
parser.add_argument('-t',
'--tokens',
type=int,
default=400,
help="Max tokens per a paragraph")
parser.add_argument('-g',
'--n_paragraphs',
type=int,
default=15,
help="Number of paragraphs to run the model on")
parser.add_argument('-f',
'--filter',
type=str,
default=None,
choices=["tfidf", "truncate", "linear"],
help="How to select paragraphs")
parser.add_argument(
'-b',
'--batch_size',
type=int,
default=200,
help="Batch size, larger sizes might be faster but wll take more memory"
)
parser.add_argument('--max_answer_len',
type=int,
default=8,
help="Max answer span to select")
parser.add_argument('-c',
'--corpus',
choices=[
"web-dev", "web-test", "web-verified-dev",
"web-train", "open-dev", "open-train", "wiki-dev",
"wiki-test"
],
default="web-verified-dev")
parser.add_argument("-s",
"--source_dir",
type=str,
default=None,
help="where to take input files")
parser.add_argument("--n_span_per_q",
type=int,
default=1,
help="where to take input files")
args = parser.parse_args()
dataset_name = args.source_dir.split('/')[-1]
model_name = args.model.split('/')[-1]
ElasticLogger().write_log('INFO',
'Start Evaluation',
context_dict={
'model': model_name,
'dataset': dataset_name
})
model_dir = ModelDir(args.model)
model = model_dir.get_model()
if args.corpus.startswith('web'):
dataset = TriviaQaWebDataset()
if args.corpus == "web-dev":
test_questions = dataset.get_dev()
elif args.corpus == "web-test":
test_questions = dataset.get_test()
elif args.corpus == "web-verified-dev":
test_questions = dataset.get_verified()
elif args.corpus == "web-train":
test_questions = dataset.get_train()
else:
raise AssertionError()
elif args.corpus.startswith("wiki"):
dataset = TriviaQaWikiDataset()
if args.corpus == "wiki-dev":
test_questions = dataset.get_dev()
elif args.corpus == "wiki-test":
test_questions = dataset.get_test()
else:
raise AssertionError()
else:
dataset = TriviaQaOpenDataset(args.source_dir)
if args.corpus == "open-dev":
# just loading the pkl that was saved in build_span_corpus
test_questions = dataset.get_dev()
elif args.corpus == "open-train":
test_questions = dataset.get_train()
else:
raise AssertionError()
### ALON debuging
#test_questions = test_questions[0:5]
corpus = dataset.evidence
splitter = MergeParagraphs(args.tokens)
per_document = args.corpus.startswith(
"web") # wiki and web are both multi-document
#per_document = True
filter_name = args.filter
if filter_name is None:
# Pick default depending on the kind of data we are using
if per_document:
filter_name = "tfidf"
else:
filter_name = "linear"
print("Selecting %d paragraphs using method \"%s\" per %s" %
(args.n_paragraphs, filter_name,
("question-document pair" if per_document else "question")))
if filter_name == "tfidf":
para_filter = TopTfIdf(NltkPlusStopWords(punctuation=True),
args.n_paragraphs)
elif filter_name == "truncate":
para_filter = FirstN(args.n_paragraphs)
elif filter_name == "linear":
para_filter = ShallowOpenWebRanker(args.n_paragraphs)
else:
raise ValueError()
n_questions = args.n_sample
docqa.config.SPANS_PER_QUESTION = args.n_span_per_q
#n_questions = 1
if n_questions is not None:
test_questions.sort(key=lambda x: x.question_id)
np.random.RandomState(0).shuffle(test_questions)
test_questions = test_questions[:n_questions]
print("Building question/paragraph pairs...")
# Loads the relevant questions/documents, selects the right paragraphs, and runs the model's preprocessor
if per_document:
prep = ExtractMultiParagraphs(splitter,
para_filter,
model.preprocessor,
require_an_answer=False)
else:
prep = ExtractMultiParagraphsPerQuestion(splitter,
para_filter,
model.preprocessor,
require_an_answer=False)
prepped_data = preprocess_par(test_questions, corpus, prep,
args.n_processes, 1000)
data = []
for q in prepped_data.data:
for i, p in enumerate(q.paragraphs):
if q.answer_text is None:
ans = None
else:
ans = TokenSpans(q.answer_text, p.answer_spans)
data.append(
DocumentParagraphQuestion(q.question_id, p.doc_id,
(p.start, p.end), q.question, p.text,
ans, i))
# Reverse so our first batch will be the largest (so OOMs happen early)
questions = sorted(data,
key=lambda x: (x.n_context_words, len(x.question)),
reverse=True)
print("Done, starting eval")
if args.step is not None:
if args.step == "latest":
checkpoint = model_dir.get_latest_checkpoint()
else:
checkpoint = model_dir.get_checkpoint(int(args.step))
else:
checkpoint = model_dir.get_best_weights()
if checkpoint is not None:
print("Using best weights")
else:
print("Using latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
test_questions = ParagraphAndQuestionDataset(
questions, FixedOrderBatcher(args.batch_size, True))
evaluation = trainer.test(
model, [RecordParagraphSpanPrediction(args.max_answer_len, True)],
{args.corpus: test_questions}, ResourceLoader(), checkpoint,
not args.no_ema, args. async)[args.corpus]
if not all(len(x) == len(data) for x in evaluation.per_sample.values()):
raise RuntimeError()
df = pd.DataFrame(evaluation.per_sample)
if args.official_output is not None:
print("Saving question result")
fns = {}
if per_document:
# I didn't store the unormalized filenames exactly, so unfortunately we have to reload
# the source data to get exact filename to output an official test script
print("Loading proper filenames")
if args.corpus == 'web-test':
source = join(TRIVIA_QA, "qa", "web-test-without-answers.json")
elif args.corpus == "web-dev":
source = join(TRIVIA_QA, "qa", "web-dev.json")
else:
raise AssertionError()
with open(join(source)) as f:
data = json.load(f)["Data"]
for point in data:
for doc in point["EntityPages"]:
filename = doc["Filename"]
fn = join("wikipedia", filename[:filename.rfind(".")])
fn = normalize_wiki_filename(fn)
fns[(point["QuestionId"], fn)] = filename
answers = {}
scores = {}
for q_id, doc_id, start, end, txt, score in df[[
"question_id", "doc_id", "para_start", "para_end",
"text_answer", "predicted_score"
]].itertuples(index=False):
filename = dataset.evidence.file_id_map[doc_id]
if per_document:
if filename.startswith("web"):
true_name = filename[4:] + ".txt"
else:
true_name = fns[(q_id, filename)]
# Alon Patch for triviaqa test results
true_name = true_name.replace('TriviaQA_Org/', '')
key = q_id + "--" + true_name
else:
key = q_id
prev_score = scores.get(key)
if prev_score is None or prev_score < score:
scores[key] = score
answers[key] = txt
with open(args.official_output, "w") as f:
json.dump(answers, f)
output_file = args.paragraph_output
if output_file is not None:
print("Saving paragraph result")
df.to_csv(output_file, index=False)
print("Computing scores")
if per_document:
group_by = ["question_id", "doc_id"]
else:
group_by = ["question_id"]
# Print a table of scores as more paragraphs are used
df.sort_values(group_by + ["rank"], inplace=True)
df_scores = df.copy(deep=True)
df_scores['predicted_score'] = df_scores['predicted_score'].apply(
lambda x: pd.Series(x).max())
em = compute_ranked_scores(df_scores, "predicted_score", "text_em",
group_by)
f1 = compute_ranked_scores(df_scores, "predicted_score", "text_f1",
group_by)
table = [["N Paragraphs", "EM", "F1"]]
table += list([str(i + 1), "%.4f" % e, "%.4f" % f]
for i, (e, f) in enumerate(zip(em, f1)))
table_df = pd.DataFrame(table[1:], columns=table[0]).drop(['N Paragraphs'],
axis=1)
ElasticLogger().write_log('INFO', 'Results', context_dict={'model': model_name, 'dataset': dataset_name, \
'max_EM':table_df.max().ix['EM'], \
'max_F1':table_df.max().ix['F1'], \
'result_table': str(table_df)})
df_flat = []
for id, question in df.iterrows():
for text_answer, predicted_span, predicted_score in zip(
question['text_answer'], question['predicted_span'],
question['predicted_score']):
new_question = dict(question.copy())
new_question.update({
'text_answer': text_answer,
'predicted_span': predicted_span,
'predicted_score': predicted_score
})
df_flat.append(new_question)
results_df = pd.DataFrame(df_flat)
#Alon: outputing the estimates for all the
#results_df = results_df.groupby(['question_id', 'text_answer']).apply(lambda df: df.ix[df['predicted_score'].argmax()]).reset_index(drop=True)
results_df.sort_values(by=['question_id', 'predicted_score'],
ascending=False).set_index([
'question_id', 'text_answer'
])[['question', 'predicted_score',
'text_em']].to_csv('results.csv')
print_table(table)
def convert(model_dir, output_dir, best_weights=False):
print("Load model")
md = ModelDir(model_dir)
model = md.get_model()
print("Setting up cudnn version")
# global_step = tf.get_variable('global_step', shape=[], dtype='int32', trainable=False)
sess = tf.Session()
with sess.as_default():
model.set_input_spec(ParagraphAndQuestionSpec(1, None, None, 14),
{"the"},
ResourceLoader(lambda a,b: {"the": np.zeros(300, np.float32)}))
print("Buiding graph")
pred = model.get_prediction()
test_questions = ParagraphAndQuestion(["Harry", "Potter", "was", "written", "by", "JK"],
["Who", "wrote", "Harry", "Potter", "?"],
None, "test_questions")
print("Load vars")
save = tf.train.Saver()
if best_weights:
checkpoint = md.get_best_weights()
else:
checkpoint = md.get_latest_checkpoint()
print("Loading checkpoint: " + checkpoint)
save.restore(sess, checkpoint)
print("Restoring EMA variables")
ema = tf.train.ExponentialMovingAverage(0)
saver = tf.train.Saver({ema.average_name(x): x for x in tf.trainable_variables()})
saver.restore(sess, checkpoint)
feed = model.encode([test_questions], False)
cuddn_out = sess.run([pred.start_logits, pred.end_logits], feed_dict=feed)
dim = 90
print("Done, copying files...")
if not exists(output_dir):
mkdir(output_dir)
for file in listdir(model_dir):
if isfile(file) and file != "model.npy":
copyfile(join(model_dir, file), join(output_dir, file))
print("Done, mapping tensors...")
to_save = []
to_init = []
for x in tf.trainable_variables():
if x.name.endswith("/gru_parameters:0"):
key = x.name[:-len("/gru_parameters:0")]
fw_params = x
if "map_embed" in x.name:
c = cudnn_rnn_ops.CudnnGRU(1, dim, 1424)
elif "chained-out" in x.name:
c = cudnn_rnn_ops.CudnnGRU(1, dim, dim * 4)
else:
c = cudnn_rnn_ops.CudnnGRU(1, dim, dim * 2)
params_saveable = cudnn_rnn_ops.RNNParamsSaveable(
c, c.params_to_canonical,
c.canonical_to_params, [fw_params],
key)
for spec in params_saveable.specs:
if spec.name.endswith("bias_cudnn 0") or \
spec.name.endswith("bias_cudnn 1"):
# ??? What do these even do?
continue
name = spec.name.split("/")
name.remove("cell_0")
if "forward" in name:
ix = name.index("forward")
name.insert(ix+2, "fw")
else:
ix = name.index("backward")
name.insert(ix + 2, "bw")
del name[ix]
ix = name.index("multi_rnn_cell")
name[ix] = "bidirectional_rnn"
name = "/".join(name)
v = tf.Variable(sess.run(spec.tensor), name=name)
to_init.append(v)
to_save.append(v)
else:
to_save.append(x)
other = [x for x in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) if x not in tf.trainable_variables()]
print(other)
sess.run(tf.initialize_variables(to_init))
saver = tf.train.Saver(to_save + other)
save_dir = join(output_dir, "save")
if not exists(save_dir):
mkdir(save_dir)
print("GLOBAL STEP HACK REMOVE ME")
saver.save(sess, join(save_dir, "checkpoint"), 42000)
sess.close()
tf.reset_default_graph()
print("Updating model...")
model.embed_mapper.layers = [model.embed_mapper.layers[0],
BiRecurrentMapper(CompatGruCellSpec(dim))]
model.match_encoder.layers = list(model.match_encoder.layers)
other = model.match_encoder.layers[1].other
other.layers = list(other.layers)
other.layers[1] = BiRecurrentMapper(CompatGruCellSpec(dim))
pred = model.predictor.predictor
pred.first_layer = BiRecurrentMapper(CompatGruCellSpec(dim))
pred.second_layer = BiRecurrentMapper(CompatGruCellSpec(dim))
with open(join(output_dir, "model.pkl"), "wb") as f:
pickle.dump(model, f)
print("Testing...")
with open(join(output_dir, "model.pkl"), "rb") as f:
model = pickle.load(f)
sess = tf.Session()
model.set_input_spec(ParagraphAndQuestionSpec(1, None, None, 14),
{"the"},
ResourceLoader(lambda a, b: {"the": np.zeros(300, np.float32)}))
pred = model.get_prediction()
print("Rebuilding")
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(save_dir))
feed = model.encode([test_questions], False)
cpu_out = sess.run([pred.start_logits, pred.end_logits], feed_dict=feed)
print("These should be close:")
print([np.allclose(a, b) for a,b in zip(cpu_out, cuddn_out)])
print(cpu_out)
print(cuddn_out)
def run():
parser = argparse.ArgumentParser()
parser.add_argument("input_data")
parser.add_argument("output_data")
parser.add_argument("--plot_dir", type=str, default=None)
parser.add_argument("--model_dir", type=str, default="/tmp/model/document-qa")
parser.add_argument("--lm_dir", type=str, default="/home/castle/data/lm/squad-context-concat-skip")
parser.add_argument("--glove_dir", type=str, default="/home/castle/data/glove")
parser.add_argument("--n", type=int, default=None)
parser.add_argument("-b", "--batch_size", type=int, default=30)
parser.add_argument("--ema", action="store_true")
args = parser.parse_args()
input_data = args.input_data
output_path = args.output_data
model_dir = ModelDir(args.model_dir)
nltk.data.path.append("nltk_data")
print("Loading data")
docs = parse_squad_data(input_data, "", NltkAndPunctTokenizer(), False)
pairs = split_docs(docs)
dataset = ParagraphAndQuestionDataset(pairs, ClusteredBatcher(args.batch_size, ContextLenKey(), False, True))
print("Done, init model")
model = model_dir.get_model()
# small hack, just load the vector file at its expected location rather then using the config location
loader = ResourceLoader(lambda a, b: load_word_vector_file(join(args.glove_dir, "glove.840B.300d.txt"), b))
lm_model = model.lm_model
basedir = args.lm_dir
plotdir = args.plot_dir
lm_model.lm_vocab_file = join(basedir, "squad_train_dev_all_unique_tokens.txt")
lm_model.options_file = join(basedir, "options_squad_lm_2x4096_512_2048cnn_2xhighway_skip.json")
lm_model.weight_file = join(basedir, "squad_context_concat_lm_2x4096_512_2048cnn_2xhighway_skip.hdf5")
lm_model.embed_weights_file = None
model.set_inputs([dataset], loader)
print("Done, building graph")
sess = tf.Session()
with sess.as_default():
pred = model.get_prediction()
best_span = pred.get_best_span(17)[0]
if plotdir != None:
start_logits_op, end_logits_op = pred.get_logits()
all_vars = tf.global_variables() + tf.get_collection(tf.GraphKeys.SAVEABLE_OBJECTS)
dont_restore_names = {x.name for x in all_vars if x.name.startswith("bilm")}
print(sorted(dont_restore_names))
vars = [x for x in all_vars if x.name not in dont_restore_names]
print("Done, loading weights")
checkpoint = model_dir.get_best_weights()
if checkpoint is None:
print("Loading most recent checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
else:
print("Loading best weights")
saver = tf.train.Saver(vars)
saver.restore(sess, checkpoint)
if args.ema:
ema = tf.train.ExponentialMovingAverage(0)
saver = tf.train.Saver({ema.average_name(x): x for x in tf.trainable_variables()})
saver.restore(sess, checkpoint)
sess.run(tf.variables_initializer([x for x in all_vars if x.name in dont_restore_names]))
print("Done, starting evaluation")
out = {}
for i, batch in enumerate(dataset.get_epoch()):
if args.n is not None and i == args.n:
break
print("On batch size [%d], now in %d th batch" % (args.batch_size, i +1))
enc = model.encode(batch, False)
if plotdir != None:
spans, start_logits, end_logits = sess.run([best_span, start_logits_op, end_logits_op], feed_dict=enc)
for bi, point in enumerate(batch):
q = ' '.join(point.question)
c = point.paragraph.get_context()
gt = ' | '.join(point.answer.answer_text)
s, e = spans[bi]
pred = point.get_original_text(s, e)
start_dist = start_logits[bi]
end_dist = end_logits[bi]
c_interval = np.arange(0.0, start_dist.shape[0], 1)
c_label = c
plt.figure(1)
plt.subplot(211)
plt.plot(c_interval, start_dist, color='r')
plt.title("Q : " + q + " // A : " + gt, fontsize=9)
plt.text(0, 0, r'Predict : %s [%d:%d]' % (pred, s, e), color='b')
axes = plt.gca()
axes.set_ylim([-20, 20])
plt.subplot(212)
plt.plot(c_interval, end_dist, color='g')
plt.xticks(c_interval, c_label, rotation=90, fontsize=5)
axes = plt.gca()
axes.set_ylim([-20, 20])
plt.show()
break
else:
spans = sess.run(best_span, feed_dict=enc)
for (s, e), point in zip(spans, batch):
out[point.question_id] = point.get_original_text(s, e)
sess.close()
print("Done, saving")
with open(output_path, "w") as f:
json.dump(out, f)
print("Mission accomplished!")
def main():
parser = argparse.ArgumentParser(description='Evaluate a model on SQuAD')
parser.add_argument('model', help='model directory to evaluate')
parser.add_argument("-o", "--official_output", type=str, help="where to output an official result file")
parser.add_argument('-n', '--sample_questions', type=int, default=None,
help="(for testing) run on a subset of questions")
parser.add_argument('--answer_bounds', nargs='+', type=int, default=[17],
help="Max size of answer")
parser.add_argument('-b', '--batch_size', type=int, default=45,
help="Batch size, larger sizes can be faster but uses more memory")
parser.add_argument('-s', '--step', default=None,
help="Weights to load, can be a checkpoint step or 'latest'")
parser.add_argument('-c', '--corpus', choices=["dev", "train"], default="dev")
parser.add_argument('--no_ema', action="store_true", help="Don't use EMA weights even if they exist")
args = parser.parse_args()
num_choices = 4
model_dir = ModelDir(args.model)
corpus = SquadCorpus()
if args.corpus == "dev":
questions = corpus.get_dev()
else:
questions = corpus.get_train()
questions = split_docs(questions)
if args.sample_questions:
np.random.RandomState(0).shuffle(sorted(questions, key=lambda x: x.question_id))
questions = questions[:args.sample_questions]
questions.sort(key=lambda x:x.n_context_words, reverse=True)
#pdb.set_trace()
#print(args.batch_size)
#dataset = ParagraphAndQuestionDataset(questions, FixedOrderBatcher(args.batch_size, False),None,num_choices)
dataset = ParagraphAndQuestionDataset(questions, ClusteredBatcher(45, ContextLenKey(), False, False),None,num_choices)
#ClusteredBatcher(45, ContextLenKey(), False, False)
evaluators = [MultiChoiceEvaluator(num_choices)]
#if args.official_output is not None:
#evaluators.append(RecordSpanPrediction(args.answer_bounds[0]))
#pdb.set_trace()
if args.step is not None:
if args.step == "latest":
checkpoint = model_dir.get_latest_checkpoint()
else:
checkpoint = model_dir.get_checkpoint(int(args.step))
else:
checkpoint = model_dir.get_best_weights()
if checkpoint is not None:
print("Using best weights")
else:
print("Using latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
model = model_dir.get_model()
#pdb.set_trace()
evaluation = trainer.test(model, evaluators, {args.corpus: dataset},
corpus.get_resource_loader(), checkpoint, not args.no_ema)[args.corpus]
#pdb.set_trace()
# Print the scalar results in a two column table
scalars = evaluation.scalars
cols = list(sorted(scalars.keys()))
table = [cols]
header = ["Metric", ""]
table.append([("%s" % scalars[x] if x in scalars else "-") for x in cols])
print_table([header] + transpose_lists(table))
# Save the official output
if args.official_output is not None:
data_to_dump = {}
list_of_choices = ['A','B','C','D']
q_ids = evaluation.per_sample["question_id"]
correct_ans = evaluation.per_sample["correct answer"]
correct_ids = evaluation.per_sample["correct index"]
pred_ids = evaluation.per_sample["predictied index"]
pred_ans = evaluation.per_sample["predictied answer"]
is_correct = evaluation.per_sample["is correct"]
#pdb.set_trace()
for ix, q_ids in enumerate(q_ids):
if(is_correct[ix]):
data_to_dump[q_ids] = {'Is Correct' : 'True',
'predictied' : [' '.join(pred_ans[ix]),list_of_choices[pred_ids[ix]]],
'correct' : [' '.join(correct_ans[ix]),list_of_choices[correct_ids[ix]]]
}
else:
data_to_dump[q_ids] = {'Is Correct' : 'False',
'predictied' : [' '.join(pred_ans[ix]),list_of_choices[pred_ids[ix]]],
'correct' : [' '.join(correct_ans[ix]),list_of_choices[correct_ids[ix]]]
}
#pdb.set_trace()
with open(args.official_output, "w") as f:
json.dump(data_to_dump , f)
