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Commit 936e813c authored by thomwolf's avatar thomwolf
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clean up examples - added squad example and test

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examples/generation_xlnet.py deleted 100644 → 0
View file @ 699bc7e8
import torch
from torch.nn import functional as F
from pytorch_transformers import XLNetModel, XLNetLMHeadModel, XLNetTokenizer
import logging
logging.basicConfig(level=logging.INFO)
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
model = XLNetLMHeadModel.from_pretrained('xlnet-large-cased', attn_type='uni')
tokens = tokenizer.encode('I am very happy')
for i in range(len(tokens), 20):
mask = torch.tensor([[[0.0] * i + [1.0]]])
logits, _ = model(torch.tensor([tokens + [0]]),
# perm_mask=mask.expand(-1, i+1, -1),
target_mapping=mask,
inp_q=mask.squeeze(1))
output = torch.multinomial(F.softmax(logits[0, 0, :]), 1)
tokens.append(output.item())
print(tokenizer.decode(tokens))
examples/run_bert_extract_features.py deleted 100644 → 0
View file @ 699bc7e8
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Extract pre-computed feature vectors from a PyTorch BERT model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import logging
import json
import re
import torch
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from pytorch_transformers.tokenization_bert import BertTokenizer
from pytorch_transformers.modeling_bert import BertModel
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO)
logger = logging.getLogger(__name__)
class InputExample(object):
def __init__(self, unique_id, text_a, text_b):
self.unique_id = unique_id
self.text_a = text_a
self.text_b = text_b
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, unique_id, tokens, input_ids, input_mask, input_type_ids):
self.unique_id = unique_id
self.tokens = tokens
self.input_ids = input_ids
self.input_mask = input_mask
self.input_type_ids = input_type_ids
def convert_examples_to_features(examples, seq_length, tokenizer):
"""Loads a data file into a list of `InputFeature`s."""
features = []
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > seq_length - 2:
tokens_a = tokens_a[0:(seq_length - 2)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambigiously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = []
input_type_ids = []
tokens.append("[CLS]")
input_type_ids.append(0)
for token in tokens_a:
tokens.append(token)
input_type_ids.append(0)
tokens.append("[SEP]")
input_type_ids.append(0)
if tokens_b:
for token in tokens_b:
tokens.append(token)
input_type_ids.append(1)
tokens.append("[SEP]")
input_type_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < seq_length:
input_ids.append(0)
input_mask.append(0)
input_type_ids.append(0)
assert len(input_ids) == seq_length
assert len(input_mask) == seq_length
assert len(input_type_ids) == seq_length
if ex_index < 5:
logger.info("*** Example ***")
logger.info("unique_id: %s" % (example.unique_id))
logger.info("tokens: %s" % " ".join([str(x) for x in tokens]))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
logger.info(
"input_type_ids: %s" % " ".join([str(x) for x in input_type_ids]))
features.append(
InputFeatures(
unique_id=example.unique_id,
tokens=tokens,
input_ids=input_ids,
input_mask=input_mask,
input_type_ids=input_type_ids))
return features
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def read_examples(input_file):
"""Read a list of `InputExample`s from an input file."""
examples = []
unique_id = 0
with open(input_file, "r", encoding='utf-8') as reader:
while True:
line = reader.readline()
if not line:
break
line = line.strip()
text_a = None
text_b = None
m = re.match(r"^(.*) \|\|\| (.*)$", line)
if m is None:
text_a = line
else:
text_a = m.group(1)
text_b = m.group(2)
examples.append(
InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b))
unique_id += 1
return examples
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--input_file", default=None, type=str, required=True)
parser.add_argument("--output_file", default=None, type=str, required=True)
parser.add_argument("--bert_model", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
## Other parameters
parser.add_argument("--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.")
parser.add_argument("--layers", default="-1,-2,-3,-4", type=str)
parser.add_argument("--max_seq_length", default=128, type=int,
help="The maximum total input sequence length after WordPiece tokenization. Sequences longer "
"than this will be truncated, and sequences shorter than this will be padded.")
parser.add_argument("--batch_size", default=32, type=int, help="Batch size for predictions.")
parser.add_argument("--local_rank",
type=int,
default=-1,
help = "local_rank for distributed training on gpus")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
args = parser.parse_args()
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device: {} n_gpu: {} distributed training: {}".format(device, n_gpu, bool(args.local_rank != -1)))
layer_indexes = [int(x) for x in args.layers.split(",")]
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
examples = read_examples(args.input_file)
features = convert_examples_to_features(
examples=examples, seq_length=args.max_seq_length, tokenizer=tokenizer)
unique_id_to_feature = {}
for feature in features:
unique_id_to_feature[feature.unique_id] = feature
model = BertModel.from_pretrained(args.bert_model)
model.to(device)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_example_index)
if args.local_rank == -1:
eval_sampler = SequentialSampler(eval_data)
else:
eval_sampler = DistributedSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.batch_size)
model.eval()
with open(args.output_file, "w", encoding='utf-8') as writer:
for input_ids, input_mask, example_indices in eval_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
all_encoder_layers, _ = model(input_ids, token_type_ids=None, attention_mask=input_mask)
all_encoder_layers = all_encoder_layers
for b, example_index in enumerate(example_indices):
feature = features[example_index.item()]
unique_id = int(feature.unique_id)
# feature = unique_id_to_feature[unique_id]
output_json = collections.OrderedDict()
output_json["linex_index"] = unique_id
all_out_features = []
for (i, token) in enumerate(feature.tokens):
all_layers = []
for (j, layer_index) in enumerate(layer_indexes):
layer_output = all_encoder_layers[int(layer_index)].detach().cpu().numpy()
layer_output = layer_output[b]
layers = collections.OrderedDict()
layers["index"] = layer_index
layers["values"] = [
round(x.item(), 6) for x in layer_output[i]
]
all_layers.append(layers)
out_features = collections.OrderedDict()
out_features["token"] = token
out_features["layers"] = all_layers
all_out_features.append(out_features)
output_json["features"] = all_out_features
writer.write(json.dumps(output_json) + "\n")
if __name__ == "__main__":
main()
examples/run_glue.py
View file @ 936e813c
......@@ -306,9 +306,9 @@ def main():
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
help="Batch size per GPU for training.")
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
help="Batch size per GPU for evaluation.")
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument("--learning_rate", default=5e-5, type=float,
......@@ -395,8 +395,7 @@ def main():
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
# Make sure only the first process in distributed training will download model & vocab
torch.distributed.barrier()
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
args.model_type = ""
for key in MODEL_CLASSES:
......@@ -409,7 +408,7 @@ def main():
model = model_class.from_pretrained(args.model_name, from_tf=bool('.ckpt' in args.model_name), config=config)
if args.local_rank == 0:
torch.distributed.barrier()
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
# Distributed and parrallel training
model.to(args.device)
......@@ -422,6 +421,7 @@ def main():
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
......@@ -450,6 +450,7 @@ def main():
tokenizer = tokenizer_class.from_pretrained(args.output_dir)
model.to(args.device)
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
......@@ -459,7 +460,7 @@ def main():
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split('-')[-1]
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=global_step)
......
examples/run_gpt2.py deleted 100644 → 0
View file @ 699bc7e8
#!/usr/bin/env python3
import argparse
import logging
from tqdm import trange
import torch
import torch.nn.functional as F
import numpy as np
from pytorch_transformers import GPT2LMHeadModel, GPT2Tokenizer
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO)
logger = logging.getLogger(__name__)
def top_k_logits(logits, k):
"""
Masks everything but the k top entries as -infinity (1e10).
Used to mask logits such that e^-infinity -> 0 won't contribute to the
sum of the denominator.
"""
if k == 0:
return logits
else:
values = torch.topk(logits, k)[0]
batch_mins = values[:, -1].view(-1, 1).expand_as(logits)
return torch.where(logits < batch_mins, torch.ones_like(logits) * -1e10, logits)
def sample_sequence(model, length, start_token=None, batch_size=None, context=None, temperature=1, top_k=0, device='cuda', sample=True):
if start_token is None:
assert context is not None, 'Specify exactly one of start_token and context!'
context = torch.tensor(context, device=device, dtype=torch.long).unsqueeze(0).repeat(batch_size, 1)
else:
assert context is None, 'Specify exactly one of start_token and context!'
context = torch.full((batch_size, 1), start_token, device=device, dtype=torch.long)
prev = context
output = context
past = None
with torch.no_grad():
for i in trange(length):
logits, past = model(prev, past=past)
logits = logits[:, -1, :] / temperature
logits = top_k_logits(logits, k=top_k)
log_probs = F.softmax(logits, dim=-1)
if sample:
prev = torch.multinomial(log_probs, num_samples=1)
else:
_, prev = torch.topk(log_probs, k=1, dim=-1)
output = torch.cat((output, prev), dim=1)
return output
def run_model():
parser = argparse.ArgumentParser()
parser.add_argument('--model_name_or_path', type=str, default='gpt2', help='pretrained model name or path to local checkpoint')
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--nsamples", type=int, default=1)
parser.add_argument("--batch_size", type=int, default=-1)
parser.add_argument("--length", type=int, default=-1)
parser.add_argument("--temperature", type=float, default=1.0)
parser.add_argument("--top_k", type=int, default=0)
parser.add_argument('--unconditional', action='store_true', help='If true, unconditional generation.')
args = parser.parse_args()
print(args)
if args.batch_size == -1:
args.batch_size = 1
assert args.nsamples % args.batch_size == 0
np.random.seed(args.seed)
torch.random.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
enc = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
model = GPT2LMHeadModel.from_pretrained(args.model_name_or_path)
model.to(device)
model.eval()
if args.length == -1:
args.length = model.config.n_ctx // 2
elif args.length > model.config.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" % model.config.n_ctx)
while True:
context_tokens = []
if not args.unconditional:
raw_text = input("Model prompt >>> ")
while not raw_text:
print('Prompt should not be empty!')
raw_text = input("Model prompt >>> ")
context_tokens = enc.encode(raw_text)
generated = 0
for _ in range(args.nsamples // args.batch_size):
out = sample_sequence(
model=model, length=args.length,
context=context_tokens,
start_token=None,
batch_size=args.batch_size,
temperature=args.temperature, top_k=args.top_k, device=device
)
out = out[:, len(context_tokens):].tolist()
for i in range(args.batch_size):
generated += 1
text = enc.decode(out[i])
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
print(text)
print("=" * 80)
else:
generated = 0
for _ in range(args.nsamples // args.batch_size):
out = sample_sequence(
model=model, length=args.length,
context=None,
start_token=enc.encoder['<|endoftext|>'],
batch_size=args.batch_size,
temperature=args.temperature, top_k=args.top_k, device=device
)
out = out[:,1:].tolist()
for i in range(args.batch_size):
generated += 1
text = enc.decode(out[i])
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
print(text)
print("=" * 80)
if __name__ == '__main__':
run_model()
examples/run_squad.py
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examples/run_xlnet_squad.py deleted 100644 → 0
View file @ 699bc7e8
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examples/test_examples.py
View file @ 936e813c
......@@ -28,6 +28,7 @@ except ImportError:
from mock import patch
import run_glue
import run_squad
logging.basicConfig(level=logging.DEBUG)
......@@ -64,6 +65,31 @@ class ExamplesTests(unittest.TestCase):
for value in result.values():
self.assertGreaterEqual(value, 0.75)
def test_run_squad(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
testargs = ["run_squad.py",
"--train_file=./examples/tests_samples/SQUAD/dev-v2.0-small.json",
"--predict_file=./examples/tests_samples/SQUAD/dev-v2.0-small.json",
"--model_name=bert-base-uncased",
"--output_dir=./examples/tests_samples/temp_dir",
"--max_steps=10",
"--warmup_steps=2",
"--do_train",
"--do_eval",
"--version_2_with_negative",
"--learning_rate=1e-4",
"--per_gpu_train_batch_size=2",
"--per_gpu_eval_batch_size=1",
"--overwrite_output_dir",
"--seed=42"]
model_name = "--model_name=bert-base-uncased"
with patch.object(sys, 'argv', testargs + [model_name]):
result = run_squad.main()
self.assertGreaterEqual(result['f1'], 30)
self.assertGreaterEqual(result['exact'], 30)
if __name__ == "__main__":
unittest.main()
examples/tests_samples/.gitignore
View file @ 936e813c
......@@ -2,4 +2,5 @@
cache*
temp*
!*.tsv
!*.json
!.gitignore
\ No newline at end of file
examples/tests_samples/SQUAD/dev-v2.0-small.json 0 → 100644
View file @ 936e813c
{
"version": "v2.0",
"data": [{
"title": "Normans",
"paragraphs": [{
"qas": [{
"question": "In what country is Normandy located?",
"id": "56ddde6b9a695914005b9628",
"answers": [{
"text": "France",
"answer_start": 159
}],
"is_impossible": false
}, {
"question": "When were the Normans in Normandy?",
"id": "56ddde6b9a695914005b9629",
"answers": [{
"text": "10th and 11th centuries",
"answer_start": 94
}],
"is_impossible": false
}, {
"question": "From which countries did the Norse originate?",
"id": "56ddde6b9a695914005b962a",
"answers": [{
"text": "Denmark, Iceland and Norway",
"answer_start": 256
}],
"is_impossible": false
}, {
"plausible_answers": [{
"text": "Rollo",
"answer_start": 308
}],
"question": "Who did King Charles III swear fealty to?",
"id": "5ad39d53604f3c001a3fe8d3",
"answers": [],
"is_impossible": true
}, {
"plausible_answers": [{
"text": "10th century",
"answer_start": 671
}],
"question": "When did the Frankish identity emerge?",
"id": "5ad39d53604f3c001a3fe8d4",
"answers": [],
"is_impossible": true
}],
"context": "The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave their name to Normandy, a region in France. They were descended from Norse (\"Norman\" comes from \"Norseman\") raiders and pirates from Denmark, Iceland and Norway who, under their leader Rollo, agreed to swear fealty to King Charles III of West Francia. Through generations of assimilation and mixing with the native Frankish and Roman-Gaulish populations, their descendants would gradually merge with the Carolingian-based cultures of West Francia. The distinct cultural and ethnic identity of the Normans emerged initially in the first half of the 10th century, and it continued to evolve over the succeeding centuries."
}, {
"qas": [{
"question": "Who was the duke in the battle of Hastings?",
"id": "56dddf4066d3e219004dad5f",
"answers": [{
"text": "William the Conqueror",
"answer_start": 1022
}],
"is_impossible": false
}, {
"plausible_answers": [{
"text": "Antioch",
"answer_start": 1295
}],
"question": "What principality did William the conquerer found?",
"id": "5ad3a266604f3c001a3fea2b",
"answers": [],
"is_impossible": true
}],
"context": "The Norman dynasty had a major political, cultural and military impact on medieval Europe and even the Near East. The Normans were famed for their martial spirit and eventually for their Christian piety, becoming exponents of the Catholic orthodoxy into which they assimilated. They adopted the Gallo-Romance language of the Frankish land they settled, their dialect becoming known as Norman, Normaund or Norman French, an important literary language. The Duchy of Normandy, which they formed by treaty with the French crown, was a great fief of medieval France, and under Richard I of Normandy was forged into a cohesive and formidable principality in feudal tenure. The Normans are noted both for their culture, such as their unique Romanesque architecture and musical traditions, and for their significant military accomplishments and innovations. Norman adventurers founded the Kingdom of Sicily under Roger II after conquering southern Italy on the Saracens and Byzantines, and an expedition on behalf of their duke, William the Conqueror, led to the Norman conquest of England at the Battle of Hastings in 1066. Norman cultural and military influence spread from these new European centres to the Crusader states of the Near East, where their prince Bohemond I founded the Principality of Antioch in the Levant, to Scotland and Wales in Great Britain, to Ireland, and to the coasts of north Africa and the Canary Islands."
}]
}, {
"title": "Computational_complexity_theory",
"paragraphs": [{
"qas": [{
"question": "What branch of theoretical computer science deals with broadly classifying computational problems by difficulty and class of relationship?",
"id": "56e16182e3433e1400422e28",
"answers": [{
"text": "Computational complexity theory",
"answer_start": 0
}],
"is_impossible": false
}, {
"plausible_answers": [{
"text": "algorithm",
"answer_start": 472
}],
"question": "What is a manual application of mathematical steps?",
"id": "5ad5316b5b96ef001a10ab76",
"answers": [],
"is_impossible": true
}],
"context": "Computational complexity theory is a branch of the theory of computation in theoretical computer science that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. A computational problem is understood to be a task that is in principle amenable to being solved by a computer, which is equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm."
}, {
"qas": [{
"question": "What measure of a computational problem broadly defines the inherent difficulty of the solution?",
"id": "56e16839cd28a01900c67887",
"answers": [{
"text": "if its solution requires significant resources",
"answer_start": 46
}],
"is_impossible": false
}, {
"question": "What method is used to intuitively assess or quantify the amount of resources required to solve a computational problem?",
"id": "56e16839cd28a01900c67888",
"answers": [{
"text": "mathematical models of computation",
"answer_start": 176
}],
"is_impossible": false
}, {
"question": "What are two basic primary resources used to guage complexity?",
"id": "56e16839cd28a01900c67889",
"answers": [{
"text": "time and storage",
"answer_start": 305
}],
"is_impossible": false
}, {
"plausible_answers": [{
"text": "the number of gates in a circuit",
"answer_start": 436
}],
"question": "What unit is measured to determine circuit simplicity?",
"id": "5ad532575b96ef001a10ab7f",
"answers": [],
"is_impossible": true
}, {
"plausible_answers": [{
"text": "the number of processors",
"answer_start": 502
}],
"question": "What number is used in perpendicular computing?",
"id": "5ad532575b96ef001a10ab80",
"answers": [],
"is_impossible": true
}],
"context": "A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying the amount of resources needed to solve them, such as time and storage. Other complexity measures are also used, such as the amount of communication (used in communication complexity), the number of gates in a circuit (used in circuit complexity) and the number of processors (used in parallel computing). One of the roles of computational complexity theory is to determine the practical limits on what computers can and cannot do."
}]
}]
}
\ No newline at end of file
examples/utils_squad.py
View file @ 936e813c
......@@ -609,6 +609,8 @@ def write_predictions(all_examples, all_features, all_results, n_best_size,
with open(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions
def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
"""Project the tokenized prediction back to the original text."""
......
examples/utils_squad_evaluate.py 0 → 100644
View file @ 936e813c
"""Official evaluation script for SQuAD version 2.0.
In addition to basic functionality, we also compute additional statistics and
plot precision-recall curves if an additional na_prob.json file is provided.
This file is expected to map question ID's to the model's predicted probability
that a question is unanswerable.
"""
import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
class EVAL_OPTS():
def __init__(self, data_file, pred_file, out_file="",
na_prob_file="na_prob.json", na_prob_thresh=1.0,
out_image_dir=None, verbose=False):
self.data_file = data_file
self.pred_file = pred_file
self.out_file = out_file
self.na_prob_file = na_prob_file
self.na_prob_thresh = na_prob_thresh
self.out_image_dir = out_image_dir
self.verbose = verbose
OPTS = None
def parse_args():
parser = argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.')
parser.add_argument('data_file', metavar='data.json', help='Input data JSON file.')
parser.add_argument('pred_file', metavar='pred.json', help='Model predictions.')
parser.add_argument('--out-file', '-o', metavar='eval.json',
help='Write accuracy metrics to file (default is stdout).')
parser.add_argument('--na-prob-file', '-n', metavar='na_prob.json',
help='Model estimates of probability of no answer.')
parser.add_argument('--na-prob-thresh', '-t', type=float, default=1.0,
help='Predict "" if no-answer probability exceeds this (default = 1.0).')
parser.add_argument('--out-image-dir', '-p', metavar='out_images', default=None,
help='Save precision-recall curves to directory.')
parser.add_argument('--verbose', '-v', action='store_true')
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
return parser.parse_args()
def make_qid_to_has_ans(dataset):
qid_to_has_ans = {}
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
qid_to_has_ans[qa['id']] = bool(qa['answers'])
return qid_to_has_ans
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
return re.sub(regex, ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def get_tokens(s):
if not s: return []
return normalize_answer(s).split()
def compute_exact(a_gold, a_pred):
return int(normalize_answer(a_gold) == normalize_answer(a_pred))
def compute_f1(a_gold, a_pred):
gold_toks = get_tokens(a_gold)
pred_toks = get_tokens(a_pred)
common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
num_same = sum(common.values())
if len(gold_toks) == 0 or len(pred_toks) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
precision = 1.0 * num_same / len(pred_toks)
recall = 1.0 * num_same / len(gold_toks)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def get_raw_scores(dataset, preds):
exact_scores = {}
f1_scores = {}
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
qid = qa['id']
gold_answers = [a['text'] for a in qa['answers']
if normalize_answer(a['text'])]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
gold_answers = ['']
if qid not in preds:
print('Missing prediction for %s' % qid)
continue
a_pred = preds[qid]
# Take max over all gold answers
exact_scores[qid] = max(compute_exact(a, a_pred) for a in gold_answers)
f1_scores[qid] = max(compute_f1(a, a_pred) for a in gold_answers)
return exact_scores, f1_scores
def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
new_scores = {}
for qid, s in scores.items():
pred_na = na_probs[qid] > na_prob_thresh
if pred_na:
new_scores[qid] = float(not qid_to_has_ans[qid])
else:
new_scores[qid] = s
return new_scores
def make_eval_dict(exact_scores, f1_scores, qid_list=None):
if not qid_list:
total = len(exact_scores)
return collections.OrderedDict([
('exact', 100.0 * sum(exact_scores.values()) / total),
('f1', 100.0 * sum(f1_scores.values()) / total),
('total', total),
])
else:
total = len(qid_list)
return collections.OrderedDict([
('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total),
('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total),
('total', total),
])
def merge_eval(main_eval, new_eval, prefix):
for k in new_eval:
main_eval['%s_%s' % (prefix, k)] = new_eval[k]
def plot_pr_curve(precisions, recalls, out_image, title):
plt.step(recalls, precisions, color='b', alpha=0.2, where='post')
plt.fill_between(recalls, precisions, step='post', alpha=0.2, color='b')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.xlim([0.0, 1.05])
plt.ylim([0.0, 1.05])
plt.title(title)
plt.savefig(out_image)
plt.clf()
def make_precision_recall_eval(scores, na_probs, num_true_pos, qid_to_has_ans,
out_image=None, title=None):
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
true_pos = 0.0
cur_p = 1.0
cur_r = 0.0
precisions = [1.0]
recalls = [0.0]
avg_prec = 0.0
for i, qid in enumerate(qid_list):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
cur_p = true_pos / float(i+1)
cur_r = true_pos / float(num_true_pos)
if i == len(qid_list) - 1 or na_probs[qid] != na_probs[qid_list[i+1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(cur_p)
recalls.append(cur_r)
if out_image:
plot_pr_curve(precisions, recalls, out_image, title)
return {'ap': 100.0 * avg_prec}
def run_precision_recall_analysis(main_eval, exact_raw, f1_raw, na_probs,
qid_to_has_ans, out_image_dir):
if out_image_dir and not os.path.exists(out_image_dir):
os.makedirs(out_image_dir)
num_true_pos = sum(1 for v in qid_to_has_ans.values() if v)
if num_true_pos == 0:
return
pr_exact = make_precision_recall_eval(
exact_raw, na_probs, num_true_pos, qid_to_has_ans,
out_image=os.path.join(out_image_dir, 'pr_exact.png'),
title='Precision-Recall curve for Exact Match score')
pr_f1 = make_precision_recall_eval(
f1_raw, na_probs, num_true_pos, qid_to_has_ans,
out_image=os.path.join(out_image_dir, 'pr_f1.png'),
title='Precision-Recall curve for F1 score')
oracle_scores = {k: float(v) for k, v in qid_to_has_ans.items()}
pr_oracle = make_precision_recall_eval(
oracle_scores, na_probs, num_true_pos, qid_to_has_ans,
out_image=os.path.join(out_image_dir, 'pr_oracle.png'),
title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)')
merge_eval(main_eval, pr_exact, 'pr_exact')
merge_eval(main_eval, pr_f1, 'pr_f1')
merge_eval(main_eval, pr_oracle, 'pr_oracle')
def histogram_na_prob(na_probs, qid_list, image_dir, name):
if not qid_list:
return
x = [na_probs[k] for k in qid_list]
weights = np.ones_like(x) / float(len(x))
plt.hist(x, weights=weights, bins=20, range=(0.0, 1.0))
plt.xlabel('Model probability of no-answer')
plt.ylabel('Proportion of dataset')
plt.title('Histogram of no-answer probability: %s' % name)
plt.savefig(os.path.join(image_dir, 'na_prob_hist_%s.png' % name))
plt.clf()
def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for i, qid in enumerate(qid_list):
if qid not in scores: continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
return 100.0 * best_score / len(scores), best_thresh
def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval['best_exact'] = best_exact
main_eval['best_exact_thresh'] = exact_thresh
main_eval['best_f1'] = best_f1
main_eval['best_f1_thresh'] = f1_thresh
def main(OPTS):
with open(OPTS.data_file) as f:
dataset_json = json.load(f)
dataset = dataset_json['data']
with open(OPTS.pred_file) as f:
preds = json.load(f)
if OPTS.na_prob_file:
with open(OPTS.na_prob_file) as f:
na_probs = json.load(f)
else:
na_probs = {k: 0.0 for k in preds}
qid_to_has_ans = make_qid_to_has_ans(dataset) # maps qid to True/False
has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
exact_raw, f1_raw = get_raw_scores(dataset, preds)
exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans,
OPTS.na_prob_thresh)
f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans,
OPTS.na_prob_thresh)
out_eval = make_eval_dict(exact_thresh, f1_thresh)
if has_ans_qids:
has_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=has_ans_qids)
merge_eval(out_eval, has_ans_eval, 'HasAns')
if no_ans_qids:
no_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=no_ans_qids)
merge_eval(out_eval, no_ans_eval, 'NoAns')
if OPTS.na_prob_file:
find_all_best_thresh(out_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans)
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(out_eval, exact_raw, f1_raw, na_probs,
qid_to_has_ans, OPTS.out_image_dir)
histogram_na_prob(na_probs, has_ans_qids, OPTS.out_image_dir, 'hasAns')
histogram_na_prob(na_probs, no_ans_qids, OPTS.out_image_dir, 'noAns')
if OPTS.out_file:
with open(OPTS.out_file, 'w') as f:
json.dump(out_eval, f)
else:
print(json.dumps(out_eval, indent=2))
return out_eval
if __name__ == '__main__':
OPTS = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
main(OPTS)
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