def create_pseudo_labeled_data(args, infer_input, infer_output, eval_result,
id2label, next_data_dir):
"""Create pseudeo labeled data for the next self-training iteration."""
dataset = datasets.concatenate_datasets([infer_input, infer_output],
axis=1)
if args.do_filter_by_confidence:
dataset = dataset.filter(
lambda example: example['probability'] > args.confidence_threshold)
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
num_selected_rows = int(eval_result * len(dataset))
print(num_selected_rows)
dataset = dataset.sort('probability', reverse=True)
dataset = dataset.select(range(num_selected_rows))
dataset = dataset.remove_columns(['label', 'probability'])
dataset = dataset.rename_column('prediction', 'label')
dataset = dataset.map(
lambda example: {'label': id2label[example['label']]})
dataset = dataset.shuffle(seed=args.seed)
pseudo_labeled_data_file = os.path.join(
next_data_dir, f'train_pseudo.{args.data_file_extension}')
if args.data_file_extension == 'csv':
dataset.to_csv(pseudo_labeled_data_file, index=False)
else:
dataset.to_json(pseudo_labeled_data_file)
def concatenate_datasets_with_ratio(args, train_dataset):
concatenate_list = []
for sub_dataset_name, ratio in zip(
args.data.sub_datasets.split(","),
args.data.sub_datasets_ratio.split(",")):
ratio = float(ratio)
sub_dataset_path = p.join(args.path.train_data_dir, sub_dataset_name)
assert p.exists(sub_dataset_path), f"{sub_dataset_name}이 존재하지 않습니다."
sub_dataset = load_from_disk(sub_dataset_path)
sub_dataset_len = int(len(sub_dataset["train"]) * ratio)
print(f"ADD SUB DATASET {sub_dataset_name}, LENGTH: {sub_dataset_len}")
# sub dataset must have same features: ['id', 'title', 'context', 'question', 'answers']
features = sub_dataset["train"].features
new_sub_dataset = sub_dataset["train"].select(range(sub_dataset_len))
new_sub_dataset = Dataset.from_pandas(new_sub_dataset.to_pandas(),
features=features)
concatenate_list.append(new_sub_dataset.flatten_indices())
train_dataset = Dataset.from_pandas(train_dataset.to_pandas(),
features=features)
train_dataset = concatenate_datasets([train_dataset.flatten_indices()] +
concatenate_list)
return train_dataset
def concate(dataset_name, data, cache_dir):
if dataset_name in dataset_types:
all_datasets_downloaded = [
load_dataset(dataset_name, sub_dataset, cache_dir=cache_dir)
for sub_dataset in dataset_types[dataset_name]
]
combined_datasets = [
concatenate_datasets(list(sub_dataset.values()))
for sub_dataset in all_datasets_downloaded
]
data = concatenate_datasets(combined_datasets)
return DatasetDict({"train": data})
data = concatenate_datasets(
list(load_dataset(dataset_name, cache_dir=cache_dir).values())
)
return DatasetDict({"train": data})
def download_dset(c, hf_tokenizer, cache_dir, num_proc):
dsets = []
ELECTRAProcessor = partial(ELECTRADataProcessor,
hf_tokenizer=hf_tokenizer,
max_length=c.max_length)
# Wikipedia
if 'wikipedia' in c.datas:
print('load/download wiki dataset')
wiki = datasets.load_dataset('wikipedia',
'20200501.en',
cache_dir=cache_dir)['train']
print('load/create data from wiki dataset for ELECTRA')
e_wiki = ELECTRAProcessor(wiki).map(
cache_file_name=f"1000_electra_wiki_{c.max_length}.arrow",
num_proc=num_proc)
dsets.append(e_wiki)
# OpenWebText
if 'openwebtext' in c.datas:
print('load/download OpenWebText Corpus')
owt = datasets.load_dataset('openwebtext',
cache_dir=cache_dir)['train']
print('load/create data from OpenWebText Corpus for ELECTRA')
e_owt = ELECTRAProcessor(owt, apply_cleaning=False).map(
cache_file_name=f"electra_owt_{c.max_length}.arrow",
num_proc=num_proc)
dsets.append(e_owt)
assert len(dsets) == len(c.datas)
train_dset = datasets.concatenate_datasets(dsets)
return train_dset
def step_3(dataset, args):
"""
Balance positive and negative samples by randomly removing examples from the larger-sized class
"""
print('In Step 3')
unq_ele_and_cts = Counter(dataset['label'])
assert len(unq_ele_and_cts) == 2, 'There should only be two unique labels'
key_with_max_val = max(unq_ele_and_cts, key=unq_ele_and_cts.get)
key_with_min_val = min(unq_ele_and_cts, key=unq_ele_and_cts.get)
extra_count = abs(unq_ele_and_cts[key_with_max_val] -
unq_ele_and_cts[key_with_min_val])
## Divide dataset into two datasets based on their labels
def filter_based_on_label(sample, idx, label):
return label == sample['label']
key_with_data = defaultdict()
for key in unq_ele_and_cts.keys():
key_with_data[key] = dataset.filter(filter_based_on_label,
fn_kwargs={'label': key},
with_indices=True)
## Remove extra counts from class with more elements
def filter_extra_samples_from_key_with_max_val(sample, idx, extra_count):
return False if idx < extra_count else True
key_with_data[key_with_max_val] = key_with_data[key_with_max_val].shuffle(
seed=args.seed).filter(filter_extra_samples_from_key_with_max_val,
fn_kwargs={'extra_count': extra_count},
with_indices=True)
## Combine the two datasets
return concatenate_datasets(list(key_with_data.values())).sort('idx')
def make_kor_dataset_v1(args):
"""KorQuad Dataset V1
1. 문서 길이 512이하 Filtering
2. Context당 Question 최대 4개
3. ans_start 위치로 8000개 샘플링
"""
kor_dataset_path = p.join(args.path.train_data_dir, "kor_dataset")
if p.exists(kor_dataset_path):
raise FileExistsError(f"{kor_dataset_path}는 이미 존재하는 파일입니다!")
kor_dataset = load_dataset("squad_kor_v1")
kor_dataset = concatenate_datasets([
kor_dataset["train"].flatten_indices(),
kor_dataset["validation"].flatten_indices()
])
# (1) 문서 길이: KLUE MRC 512가 최소 길이
kor_dataset = filtering_by_doc_len(kor_dataset, doc_len=512)
# (2) 중복 Context 제거: Context당 최대 4개의 질문
kor_dataset = filtering_by_dup_question(kor_dataset, dup_limit=4)
# (3) KOR answer_start Weight Sampling 2배수 사용
kor_dataset = sampling_by_ans_start_weights(kor_dataset, sample=8000)
# (4) KOR_DATASET만 저장
kor_datasets = DatasetDict({"train": kor_dataset})
kor_datasets.save_to_disk(kor_dataset_path)
print(f"{kor_dataset_path}에 저장되었습니다!")
def load_and_concatenate_datasets(data_args):
"""Load and concatenate multiple compatible datasets"""
train_datasets, validation_datasets = [], []
for name, config in zip(data_args.dataset_name,
data_args.dataset_config_name):
dataset = load_dataset(name, config)
if "validation" not in dataset.keys():
validation_ds = load_dataset(
name,
config,
split=f"train[:{data_args.validation_split_percentage}%]",
)
train_ds = load_dataset(
name,
config,
split=f"train[{data_args.validation_split_percentage}%:]",
)
else:
validation_ds = dataset["validation"]
train_ds = dataset["train"]
# Some specific preprocessing to align fields on known datasets
# extraneous fields not used in language modeling are also removed
# after preprocessing
if name == "wikipedia":
train_ds.remove_columns_("title")
validation_ds.remove_columns_("title")
elif name == "ptb_text_only":
train_ds.rename_column_("sentence", "text")
validation_ds.rename_column_("sentence", "text")
train_datasets.append(train_ds)
validation_datasets.append(validation_ds)
for ds_idx in range(1, len(train_datasets)):
assert train_datasets[ds_idx].features.type == \
train_datasets[ds_idx - 1].features.type, \
"Features name and type must match between all datasets"
datasets = DatasetDict()
datasets["train"] = concatenate_datasets(train_datasets)
datasets["validation"] = concatenate_datasets(validation_datasets)
return datasets
def from_datasets(cls, dataset_list: List["Dataset"]) -> "Dataset":
"""Create a single Dataset by concatenating a list of datasets
Parameters
----------
dataset_list
Datasets to be concatenated. They must have the same column types.
Returns
-------
dataset
"""
return cls(datasets.concatenate_datasets([ds.dataset for ds in dataset_list]))
def test_dataset_concatenate_audio_features(shared_datadir):
# we use a different data structure between 1 and 2 to make sure they are compatible with each other
audio_path = str(shared_datadir / "test_audio_44100.wav")
data1 = {"audio": [audio_path]}
dset1 = Dataset.from_dict(data1, features=Features({"audio": Audio()}))
data2 = {"audio": [{"bytes": open(audio_path, "rb").read()}]}
dset2 = Dataset.from_dict(data2, features=Features({"audio": Audio()}))
concatenated_dataset = concatenate_datasets([dset1, dset2])
assert len(concatenated_dataset) == len(dset1) + len(dset2)
assert concatenated_dataset[0]["audio"]["array"].shape == dset1[0][
"audio"]["array"].shape
assert concatenated_dataset[1]["audio"]["array"].shape == dset2[0][
"audio"]["array"].shape
def add_index(shard_dir, index_path):
data_shard_list = []
for shard_address in glob(str(shard_dir) + "/*/"):
data_shard_list.append(load_from_disk(shard_address))
concat = concatenate_datasets(data_shard_list)
faiss.omp_set_num_threads(96)
index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)
concat.add_faiss_index("embeddings", custom_index=index)
concat.get_index("embeddings").save(
index_path
) # since we load the index in to memory,we can directly update the index in the disk
def extend_length(self, length):
"""
Extends the length of the dataset by randomly repeating length amount of rows.
"""
len_ = len(self)
if length < len_:
raise('Should not decrease the length of dataset')
rand_indices = np.random.randint(len_, size=length - len_)
columns = self.dataset.format['columns']
additional_data = self.dataset.select(rand_indices)
self.dataset = concatenate_datasets([self.dataset, additional_data])
self.dataset.set_format(type=self.dataset.format["type"], columns=columns)
def process_hf_dataset(self,
training_ds: Dataset,
validation_ds: Optional[Dataset] = None):
ds = training_ds
# concatenate the validation dataset if it is included
if validation_ds is not None:
# add an "is_valid_col" column to both training/validation DataFrames to indicate what data is part of
# the validation set
if self.is_valid_attr:
validation_ds = validation_ds.add_column(
self.is_valid_attr, [True] * len(validation_ds))
training_ds = training_ds.add_column(
self.is_valid_attr, [False] * len(training_ds))
ds = concatenate_datasets([training_ds, validation_ds])
return ds
def create_dataloader(args,
dataset,
tokenizer,
k_shot=False,
num_classes=None):
"""
Function to create a PyTorch Dataloader from a given dataset.
Inputs:
args - Namespace object from the argument parser
dataset - Dataset to convert to Dataloader
tokenizer - BERT tokenizer instance
k_shot - Indicates whether to make the training set k-shot. Default is False
num_classes - Number of classes in the dataset. Default is None
Outputs:
dataset - DataLoader object of the dataset
"""
# check if k-shot
new_dataset = []
if k_shot:
for current_class in range(0, num_classes):
class_set = dataset.filter(
lambda example: example['labels'] == current_class)
class_set = class_set.shuffle()
class_set = class_set.filter(lambda e, i: i < args.k,
with_indices=True)
new_dataset.append(class_set)
dataset = concatenate_datasets(new_dataset)
# create a data collator function
data_collator = DataCollatorWithPadding(tokenizer)
# create the dataloader
dataset = DataLoader(
dataset,
batch_size=args.batch_size,
collate_fn=data_collator,
drop_last=False,
shuffle=True,
)
# return the dataset
return dataset
def load_qa_dataset(examples_dirpath, scratch_dirpath, clean_model_filepaths=None, more_clean_data=False):
clean_fns = []
if more_clean_data:
for model_type_paths in clean_model_filepaths.values():
clean_examples_dirpath_list = ['/'.join(v.split('/')[:-1]+['example_data']) for v in model_type_paths]
for dirpath in clean_examples_dirpath_list:
clean_fns += [os.path.join(dirpath, fn) for fn in os.listdir(dirpath) if (fn.endswith('.json') and 'clean'in fn)]
fns = [os.path.join(examples_dirpath, fn) for fn in os.listdir(examples_dirpath) if (fn.endswith('.json') and 'clean'in fn)]
fns.sort()
examples_filepath_list = fns + clean_fns
dataset_list = []
for examples_filepath in examples_filepath_list:
# Load the examples
dataset_list.append(datasets.load_dataset('json', data_files=[examples_filepath], field='data', keep_in_memory=True, split='train', cache_dir=os.path.join(scratch_dirpath, '.cache')))
return datasets.concatenate_datasets(dataset_list)
def _exec_logistic_regression(self):
datasets = load_from_disk(
p.join(self.args.path.train_data_dir, "train_dataset"))
train_dataset = concatenate_datasets(
[datasets["train"], datasets["validation"]])
queries = train_dataset["question"]
doc_scores, doc_indices = self.sparse_retriever.get_relevant_doc_bulk(
queries, topk=8)
doc_scores, doc_indices = np.array(doc_scores), np.array(doc_indices)
contexts = np.array(self.sparse_retriever.contexts)
train_x, train_y = [], []
for idx in tqdm.tqdm(range(len(doc_scores))):
doc_index = doc_indices[idx]
org_context = train_dataset["context"][idx]
feature_vector = [
doc_scores[idx][:pow(2, i)]
for i in range(1, self.num_features + 1)
]
feature_vector = list(map(lambda x: x.mean(), feature_vector))
feature_vector = softmax(feature_vector)
label = 0
y = -1
if org_context in contexts[doc_index]:
y = list(contexts[doc_index]).index(org_context)
if y != -1 and y < self.kbound:
label = 1
train_x.append(feature_vector)
train_y.append(label)
logistic = LogisticRegression()
logistic.fit(train_x, train_y)
return logistic
def main(args):
mrc_test_dataset = load_from_disk(p.join(args.path.train_data_dir, "test_dataset"))
mrc_dummy_dataset = load_from_disk(p.join(args.path.train_data_dir, "dummy_dataset"))
all_mrc_dummy_dataset = concatenate_datasets(
[mrc_dummy_dataset["train"].flatten_indices(), mrc_dummy_dataset["validation"].flatten_indices()]
)
cheat_ids = list(set(mrc_test_dataset["validation"]["id"]).intersection(set(all_mrc_dummy_dataset["id"])))
cheats = {}
for cheat_id in cheat_ids: # ex) cheat_id: 'mrc-1-000711'
temp = check_is_real_cheating(mrc_test_dataset, all_mrc_dummy_dataset, cheat_id)
cheats[cheat_id] = temp
cheat_path = p.join(args.path.train_data_dir, "cheat.json")
print(cheats)
with open(cheat_path, "w") as f:
f.write(json.dumps(cheats, indent=4, ensure_ascii=False) + "\n")
def createDataset(config):
"""
build dataset from the h5 file
also filter out rare *individual ATU*
"""
df = pd.read_hdf(config.data.h5_file, key=config.data.h5_key)
atu = df.loc[df.groupby("atu")["atu"].filter(
lambda g: len(g) >= config["datamodules"]["atu_filter_no"]).index]
atu = atu[["text", "atu", "desc", "label"]]
dataset = Dataset.from_pandas(atu)
tokenizer = AutoTokenizer.from_pretrained(config["module"]["arch"])
def tokenize(instance):
return tokenizer(instance["text"],
max_length=config["module"]["seq_len"],
truncation="longest_first",
padding="max_length")
dataset = dataset. \
shuffle(seed=config.seed). \
map(tokenize, batched=True)
# split by cls (stratified)
sub_ds = {"train": [], "test": []}
for cls in np.unique(dataset["label"]):
cls_ds = dataset. \
filter(lambda d: d['label'] == int(cls))
cls_ds = cls_ds.train_test_split(test_size=config.data.test_ratio,
seed=config.seed)
sub_ds["train"].append(cls_ds["train"])
sub_ds["test"].append(cls_ds["test"])
dataset = DatasetDict(
{split: concatenate_datasets(ds)
for split, ds in sub_ds.items()})
dataset.save_to_disk(config.data.cached_dir)
return dataset
cols=TEXT_COLS[task],
max_len=c.max_length)
glue_dsets[task] = dsets.my_map(
tok_func, cache_file_names=f"tokenized_{c.max_length}_{{split}}")
if c.double_unordered and task in ["mrpc", "stsb"]:
swap_tok_func = partial(
tokenize_sents_max_len,
cols=TEXT_COLS[task],
max_len=c.max_length,
swap=True,
)
swapped_train = dsets["train"].my_map(
swap_tok_func,
cache_file_name=f"swapped_tokenized_{c.max_length}_train")
glue_dsets[task]["train"] = datasets.concatenate_datasets(
[glue_dsets[task]["train"], swapped_train])
# Load / Make dataloaders
hf_dsets = HF_Datasets(
glue_dsets[task],
hf_toker=hf_tokenizer,
n_inp=3,
cols={
"inp_ids": TensorText,
"attn_mask": noop,
"token_type_ids": noop,
"label": TensorCategory,
},
)
if c.double_unordered and task in ["mrpc", "stsb"]:
dl_kwargs = {
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
wiki = load_dataset("wikipedia", "20200501.en", split="train") # len(wiki):6078422
bookcorpus = load_dataset("bookcorpus", split="train") # len(bookcorpus): 74004228
print(wiki.column_names, bookcorpus.column_names)
# ['title', 'text'] ['text']
wiki.remove_columns_("title")
bart_dataset = concatenate_datasets([wiki, bookcorpus]) # len(bart_dataset):80082650
tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large', use_fast=True)
# Preprocessing the datasets.
# First we tokenize all the texts.
column_names = bart_dataset.column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name])
tokenized_datasets = bart_dataset.map(
tokenize_function,
batched=True,
num_proc=16,
timit = load_dataset('timit_asr')
def is_discardable(batch):
for phoneme in batch["phonetic_detail"]["utterance"]:
if phoneme in TIMIT_DISCARD:
return False
return True
timit_filt = timit["train"].filter(lambda eg: is_discardable(eg))
timit_filt2 = timit["test"].filter(lambda eg: is_discardable(eg))
timit = concatenate_datasets([timit_filt, timit_filt2])
MAX_TOKENS = 1120000
BASE = timit[0]["file"].split("/data/")[0] + "/data/"
with open("train_timit.tsv", "w") as manifest, open("train_timit.ltr",
"w") as transcript:
manifest.write(BASE + "\n")
for item in timit:
frames, sr = sf.read(item["file"])
manifest.write(f"{item['file'].replace(BASE, '')}\t{len(frames)}\n")
utt = item['phonetic_detail']['utterance']
mapped = map_timit_to_cmudict(utt)
transcript.write(f"{' '.join(mapped)}\n")
parser.add_argument("output_dir")
parser.add_argument("--lr", default=3e-5, type=float)
parser.add_argument("--epochs", default=3, type=int)
parser.add_argument("--batch_size", default=128, type=int)
parser.add_argument("--fp16", action="store_true")
args = parser.parse_args()
# Load dataset. TODO: Do this in prepare_smnli.py instead
dataset_list = [
load_from_disk("data/mnli-tokenized"),
load_from_disk("data/snli-tokenized"),
]
train_dataset = concatenate_datasets([
Dataset.from_dict({
"attention_mask": dataset["train"]["attention_mask"],
"input_ids": dataset["train"]["input_ids"],
"token_type_ids": dataset["train"]["token_type_ids"],
"label": dataset["train"]["label"],
}) for dataset in dataset_list
])
def compute_metrics(p: EvalPrediction):
preds = p.predictions[0] if isinstance(p.predictions,
tuple) else p.predictions
preds = np.argmax(preds, axis=1)
return {
"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()
}
# Prepare and train model
def get_train_dataset(args):
args.data.dataset_name = "train_dataset"
datasets = get_dataset(args, is_train=True)
datasets = concatenate_datasets(
[datasets["train"], datasets["validation"]])
return datasets
doc_scores = []
doc_indices = []
for query in queries:
ret0, ret1 = self.get_relevant_doc(query, k)
doc_scores.append(ret0)
doc_indices.append(ret1)
return doc_scores, doc_indices
if __name__ == "__main__":
# Test sparse
org_dataset = load_from_disk("data/train_dataset")
full_ds = concatenate_datasets([
org_dataset["train"].flatten_indices(),
org_dataset["validation"].flatten_indices(),
]) # train dev 를 합친 4192 개 질문에 대해 모두 테스트
print("*" * 40, "query dataset", "*" * 40)
print(full_ds)
mecab = Mecab()
def tokenize(text):
# return text.split(" ")
return mecab.morphs(text)
wiki_path = "wikipedia_documents.json"
retriever = BM25Arti(
# tokenize_fn=tokenizer.tokenize,
tokenize_fn=tokenize,
data_path="data",
if not os.path.exists(train_save_path):
print("generating new, class-balanced datasets...")
train_datasets = []
valid_datasets = []
for c in range(num_classes):
class_dataset = dataset.filter(
lambda example: example['label'] == c).shuffle()
train_datasets.append(
class_dataset.select(range(num_train_per_class)))
if not os.path.exists(valid_save_path):
valid_datasets.append(
class_dataset.select(
range(num_train_per_class,
num_valid_per_class + num_train_per_class)))
train_dataset = concatenate_datasets(train_datasets).shuffle()
train_dataset.save_to_disk(train_save_path)
if not os.path.exists(valid_save_path):
valid_dataset = concatenate_datasets(valid_datasets).shuffle()
valid_dataset.save_to_disk(valid_save_path)
else:
print("loading {}...".format(train_save_path))
# abridged, class-balanced dataset already exists, so just load it
train_dataset = load_from_disk(train_save_path)
for t in ts:
tran_train_save_path = os.path.join(
data_dir, task, t, task + '_train_' + str(num_train_per_class))
dataset_448.save_to_disk("/home/ahemf/processed/c4_448")
c4 = DatasetDict.load_from_disk("/home/ahemf/processed/c4_448")
dsets = Dataset.load_from_disk("/home/ahemf/processed/dsets_448")
c4['train'] = c4['train'].add_column('dataset', ['c4'] * len(c4['train']))
c4['train'] = c4['train'].remove_columns(['url', 'timestamp'])
c4['validation'] = c4['validation'].remove_columns(['url', 'timestamp'])
c4['validation'] = c4['validation'].add_column('dataset',
['c4'] * len(c4['validation']))
dataset_col = dsets['dataset']
dsets = dsets.remove_columns(["dataset"])
dsets = dsets.add_column("dataset", dataset_col)
c4["train"] = concatenate_datasets([c4["train"], dsets])
c4["train"].save_to_disk("/home/ahemf/processed/c4_extended")
c4 = Dataset.load_from_disk("/home/ahemf/processed/c4_extended")
###################################################################
## TF-IDF
###################################################################
from collections import Counter
from transformers import AutoTokenizer, AutoModel, RobertaTokenizerFast
from datasets import load_dataset, concatenate_datasets, Dataset, DatasetDict
import os
import numpy as np
import re
from multiprocess.pool import Pool
def __get_all_train(self):
all_data = concatenate_datasets([self.snli['train'], self.mnli['train']])
return self.__get_data_loader(all_data)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser(
(BasicModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(
json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses(
)
checkpoint_dir = hyperparam_path_for_baseline(model_args, data_args,
training_args)
ckpt_dir = Path(checkpoint_dir)
postfix = ""
if training_args.do_train:
postfix += "_train"
if training_args.do_eval:
postfix += "_eval"
setup_root_logger(ckpt_dir,
training_args.local_rank,
debug=False,
postfix=postfix)
training_args.output_dir = checkpoint_dir
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s", training_args)
# Set seed before initializing model.
set_seed(training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if not 0 <= data_args.holdout_set < data_args.n_fold:
raise ValueError("Test fold must be in [0, n_fold)")
if data_args.dataset not in ['race', 'dream']:
raise ValueError("Dataset should be race or dream.")
else:
from mcmrc.data_utils.processors import prepare_features
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
if data_args.debug_mode:
datasets = load_dataset(data_args.dataload_script,
data_args.dataload_split,
data_dir=data_args.data_dir,
split={
'train':
ReadInstruction('train',
from_=0,
to=5,
unit='abs'),
'validation':
ReadInstruction('validation',
from_=0,
to=5,
unit='abs'),
'test':
ReadInstruction('test',
from_=0,
to=5,
unit='abs')
})
else:
datasets = load_dataset(data_args.dataload_script,
data_args.dataload_split,
data_dir=data_args.data_dir)
if data_args.shuffle_train_dataset:
datasets['train'] = datasets['train'].shuffle(seed=training_args.seed)
if data_args.split_train_dataset:
holdout_set_start = int(
len(datasets['train']) / data_args.n_fold * data_args.holdout_set)
holdout_set_end = int(
len(datasets['train']) / data_args.n_fold *
(data_args.holdout_set + 1))
shuffled_train_set = datasets['train'].shuffle(seed=training_args.seed)
if holdout_set_start == 0:
new_train_set = Dataset.from_dict(
shuffled_train_set[holdout_set_end:])
elif holdout_set_end == len(datasets['train']):
new_train_set = Dataset.from_dict(
shuffled_train_set[:holdout_set_start])
else:
new_train_set = concatenate_datasets([
Dataset.from_dict(shuffled_train_set[:holdout_set_start]),
Dataset.from_dict(shuffled_train_set[holdout_set_end:])
])
new_holdout_set = Dataset.from_dict(
shuffled_train_set[holdout_set_start:holdout_set_end])
assert new_train_set.num_rows + new_holdout_set.num_rows == shuffled_train_set.num_rows
datasets['train'] = new_train_set
datasets['holdout_set'] = new_holdout_set
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name
if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name
if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
)
model = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
if training_args.do_train:
column_names = datasets["train"].column_names
else:
column_names = datasets["validation"].column_names
pprepare_features = partial(prepare_features,
tokenizer=tokenizer,
data_args=data_args)
tokenized_datasets = datasets.map(
pprepare_features,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
)
# Data collator
data_collator = (default_data_collator if data_args.pad_to_max_length else
DataCollatorForMultipleChoice(tokenizer=tokenizer))
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"]
if training_args.do_train else None,
eval_dataset=tokenized_datasets["validation"]
if training_args.do_eval else None,
tokenizer=tokenizer,
data_collator=data_collator,
compute_metrics=compute_mc_metrics,
)
# Training
if training_args.do_train:
train_result = trainer.train()
output_train_file = os.path.join(training_args.output_dir,
"train_results.txt")
with open(output_train_file, "w") as writer:
logger.info("***** Train results *****")
for key, value in sorted(train_result.metrics.items()):
logger.info(f"{key} = {value:.3f}")
writer.write(f"{key} = {value:.3f}\n")
# Evaluation
# To use the best checkpoint model at end, use the aruguments
# load_best_model_at_end, metric_for_best_model, evaluation_strategy steps
# --load_best_model_at_end \
# --metric_for_best_model accuracy \
# --evaluation_strategy steps \
if training_args.do_eval:
if training_args.load_best_model_at_end:
best_model = AutoModelForMultipleChoice.from_pretrained(
training_args.output_dir,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
best_model = best_model.to(training_args.device)
for split in [k for k in datasets.keys() if k != "train"]:
logger.info(f"*** Evaluate {split} set ***")
results = trainer.evaluate(tokenized_datasets[split])
if training_args.load_best_model_at_end:
final_model = trainer.model
trainer.model = best_model
best_model_results = trainer.evaluate(
tokenized_datasets[split])
trainer.model = final_model
output_eval_file = os.path.join(training_args.output_dir,
f"{split}_results.txt")
with open(output_eval_file, "a+") as writer:
logger.info("***** Extensive Eval results *****")
if not training_args.do_train:
writer.write(
f"eval checkpoint {model_args.model_name_or_path}\n")
for key, value in sorted(results.metrics.items()):
logger.info(f"{key} = {value:.3f}")
writer.write(f"{key} = {value:.3f}\n")
if training_args.load_best_model_at_end:
writer.write(f"best model on dev set\n")
for key, value in sorted(
best_model_results.metrics.items()):
logger.info(f"{key} = {value:.3f}")
writer.write(f"{key} = {value:.3f}\n")
if data_args.output_prediction_file or data_args.split_train_dataset:
prediction = {
example_id: prediction.tolist()
for prediction, label_id, example_id in zip(*results[:-1])
}
if split == "holdout_set":
output_prediction_file = os.path.join(
training_args.output_dir,
f"holdout_{data_args.n_fold}_{data_args.holdout_set}_prediction.json"
)
else:
output_prediction_file = os.path.join(
training_args.output_dir, f"{split}_prediction.json")
with open(output_prediction_file, "w") as f:
json.dump(prediction, f)
def training_step(self, batch, batch_idx) -> Dict:
global isEmUpdateBusy # use to check whether the entire embedding update process is finished or not
global isAddIndexBusy # use to check whether the entire indexing process is finished or not
global processes # use to keep threads embedding update processes
global threadHandle_index # use to keep thread in embedding indexing processes
if (self.trainer.global_rank == 0) and (self.custom_config.end2end):
if (not batch_idx == 0) and (
batch_idx % self.custom_config.indexing_freq == 0):
free_gpu_list = []
nvmlInit()
deviceCount = nvmlDeviceGetCount()
my_list = json.loads(self.custom_config.gpu_order)
for i in range(deviceCount):
handle = nvmlDeviceGetHandleByIndex(i)
info = nvmlDeviceGetMemoryInfo(handle)
if info.used / 1e6 < 15:
position = my_list.index(i)
free_gpu_list.append("cuda:" + str(position))
if len(free_gpu_list) >= self.custom_config.index_gpus:
has_free_gpus = True
else:
has_free_gpus = False
if (not isEmUpdateBusy) and has_free_gpus:
model_copy = type(self.model.rag.ctx_encoder)(
self.config_dpr
) # get a new instance #this will be load in the CPU
model_copy.load_state_dict(self.model.rag.ctx_encoder.
state_dict()) # copy weights
processes = []
if len(free_gpu_list) > self.custom_config.index_gpus:
cuda_devices = random.sample(
free_gpu_list, self.custom_config.index_gpus)
else:
cuda_devices = free_gpu_list
num_processes = len(cuda_devices)
for rank in range(num_processes):
logger.info(
"Iniitializing embedding calculation process rank{}"
.format(rank))
device = cuda_devices[rank]
p = multiprocessing.Process(
target=embed_update,
args=(
copy.deepcopy(model_copy),
num_processes,
device,
rank,
self.custom_config.shard_dir,
self.custom_config.csv_path,
),
)
processes.append(p)
for p in processes:
p.start()
isEmUpdateBusy = True
if isEmUpdateBusy and (not isAddIndexBusy):
index_process_list = [
processes[k].is_alive()
for k in range(self.custom_config.index_gpus)
]
if (
sum(index_process_list) == 0
): # If entire list is false, we can say all embedding calculation process has finished
logger.info("Start adding the index")
threadHandle_index = multiprocessing.Process(
target=add_index,
args=(
self.custom_config.shard_dir,
self.config.index_path,
),
)
threadHandle_index.start()
isAddIndexBusy = True
# check when index building has started
if isAddIndexBusy:
# check still the index_building process is happening
if not threadHandle_index.is_alive():
logger.info("Merging the dataset shards")
saved_dataset_shards = []
for address in glob(
str(self.custom_config.shard_dir) + "/*/"):
saved_dataset_shards.append(load_from_disk(address))
concat = concatenate_datasets(saved_dataset_shards)
concat.save_to_disk(
self.config.passages_path
) # here we update the main passage file on the disk
logger.info("done updating the dataset")
# if you load the index from the disk make sure to update the index file here, otherwise it is ok to update the index file from the worker.
# logger.info("then updating the index")
# shutil.copy(self.custom_config.temp_index, self.config.idex_path)
logger.info(
"Loading new passages and iniitalzing new index")
self.trainer.model.module.module.model.rag.retriever.re_load(
)
self.trainer.model.module.module.model.rag.retriever.init_retrieval(
)
isEmUpdateBusy = False
isAddIndexBusy = False
self.trainer.accelerator_connector.accelerator.barrier(
"barrier") # waint untill the index and kb get re-initialized.
loss_tensors = self._step(batch)
logs = {
name: loss
for name, loss in zip(self.loss_names, loss_tensors)
}
# tokens per batch
tgt_pad_token_id = (self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer, RagTokenizer) else
self.tokenizer.pad_token_id)
src_pad_token_id = (self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer, RagTokenizer) else
self.tokenizer.pad_token_id)
logs["tpb"] = (batch["input_ids"].ne(src_pad_token_id).sum() +
batch["decoder_input_ids"].ne(tgt_pad_token_id).sum())
self.log("loss", loss_tensors[0])
return loss_tensors[0]
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(
json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses(
)
if (os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir) and training_args.do_train
and not training_args.overwrite_output_dir):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome.")
if not os.path.exists(training_args.output_dir):
os.makedirs(training_args.output_dir)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO
if is_main_process(training_args.local_rank) else logging.WARN,
)
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info(f"Training/evaluation parameters {training_args}")
# Set seed before initializing model.
set_seed(training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
# sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
# label if at least two columns are provided.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.task_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset("glue", data_args.task_name)
elif data_args.train_file.endswith(".csv"):
# Loading a dataset from local csv files
datasets = load_dataset("csv",
data_files={
"train": data_args.train_file,
"validation": data_args.validation_file
})
else:
if not data_args.joint_training:
# Loading a dataset from local json files
datasets = load_dataset("json",
data_files={
"train": data_args.train_file,
"validation": data_args.validation_file
})
else:
# joint training
datasets = {}
for lang in MARC_LANGS:
datasets[lang] = load_dataset(
"json",
data_files={
"train": data_args.train_file.format(lang),
"validation": data_args.validation_file.format(lang)
})
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
if data_args.task_name is not None:
is_regression = data_args.task_name == "stsb"
if not is_regression:
label_list = datasets["train"].features["label"].names
num_labels = len(label_list)
else:
num_labels = 1
else:
# Trying to have good defaults here, don't hesitate to tweak to your needs.
# if not data_args.joint_training:
tmp = datasets["en"] if data_args.joint_training else datasets
is_regression = tmp["train"].features[
data_args.label_column_name].dtype in ["float32", "float64"]
if is_regression:
num_labels = 1
else:
# A useful fast method:
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
label_list = tmp["train"].unique(data_args.label_column_name)
label_list.sort() # Let's sort it for determinism
num_labels = len(label_list)
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
# Preprocessing the datasets
if data_args.task_name is not None:
sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
else:
# Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
tmp = datasets["en"] if data_args.joint_training else datasets
non_label_column_names = [
name for name in tmp["train"].column_names
if name != data_args.label_column_name
]
if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
sentence1_key, sentence2_key = "sentence1", "sentence2"
else:
#### completely modify this condition to use MARC
sentence1_key, sentence2_key = "review_body", None
# if len(non_label_column_names) >= 2:
# sentence1_key, sentence2_key = non_label_column_names[:2]
# else:
# sentence1_key, sentence2_key = non_label_column#### completely modify this condition to use MARC _names[0], None
#### completely modify this condition to use MARC
# Padding strategy
if data_args.pad_to_max_length:
padding = "max_length"
max_length = data_args.max_seq_length
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
padding = False
max_length = None
# idx2token = {v: k for k, v in tokenizer.vocab.items()}
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name
if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
)
# Some models have set the order of the labels to use, so let's make sure we do use it.
label_to_id = {v: i for i, v in enumerate(label_list)}
# if (
# model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
# and data_args.task_name is not None
# and is_regression
# ):
# # Some have all caps in their config, some don't.
# label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
# if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
# label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)}
# else:
# logger.warning(
# "Your model seems to have been trained with labels, but they don't match the dataset: ",
# f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
# "\nIgnoring the model labels as a result.",
# )
# elif data_args.task_name is None:
# label_to_id = {v: i for i, v in enumerate(label_list)}
def preprocess_function(examples):
# Tokenize the texts
args = ((examples[sentence1_key], ) if sentence2_key is None else
(examples[sentence1_key], examples[sentence2_key]))
### change tokenizer language code ###
lang = examples["language"][0]
lang_code = LANG2LANG_CODE[lang]
tokenizer.set_src_lang_special_tokens(lang_code)
### change tokenizer language code ###
result = tokenizer(*args,
padding=padding,
max_length=max_length,
truncation=True)
# Map labels to IDs (not necessary for GLUE tasks)
if label_to_id is not None and data_args.label_column_name in examples:
result["label"] = [
label_to_id[l] for l in examples[data_args.label_column_name]
]
return result
if not data_args.joint_training:
datasets = datasets.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache)
else:
for lang in MARC_LANGS:
datasets[lang] = datasets[lang].map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache)
merged_train_datasets = datasets_module.concatenate_datasets(
[dataset["train"] for dataset in datasets.values()])
merged_dev_datasets = datasets_module.concatenate_datasets(
[dataset["validation"] for dataset in datasets.values()])
datasets = DatasetDict(train=merged_train_datasets,
validation=merged_dev_datasets)
train_dataset = datasets["train"]
eval_dataset = datasets["validation_matched" if data_args.task_name ==
"mnli" else "validation"]
if data_args.task_name is not None:
test_dataset = datasets["test_matched" if data_args.task_name ==
"mnli" else "test"]
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(
f"Sample {index} of the training set: {train_dataset[index]}.")
# Get the metric function
if data_args.task_name is not None:
metric = load_metric("glue", data_args.task_name)
# TODO: When datasets metrics include regular accuracy, make an else here and remove special branch from
# compute_metrics
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(p: EvalPrediction):
# TODO: modify accordingly
preds = p.predictions[0] if isinstance(p.predictions,
tuple) else p.predictions
preds = np.squeeze(preds) if is_regression else np.argmax(preds,
axis=1)
if data_args.task_name is not None:
result = metric.compute(predictions=preds, references=p.label_ids)
if len(result) > 1:
result["combined_score"] = np.mean(list(
result.values())).item()
return result
elif is_regression:
### modify from MSE to MAE for MARC ###
return {"mae": (np.abs(preds - p.label_ids)).mean().item()}
### modify from MSE to MAE for MARC ###
else:
return {
"accuracy":
(preds == p.label_ids).astype(np.float32).mean().item(),
"mae": (np.abs(preds - p.label_ids)).mean().item()
}
NUM_LAYERS = data_args.num_layer
LAYERS = list(range(2 * NUM_LAYERS))
NUM_HEADS = data_args.num_head
for layer in LAYERS:
heads = range(
2 * NUM_HEADS) if layer > NUM_LAYERS - 1 else range(NUM_HEADS)
for head in heads:
config = AutoConfig.from_pretrained(
model_args.config_name
if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
)
model = MBartForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset if training_args.do_eval else None,
compute_metrics=compute_metrics,
tokenizer=tokenizer,
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
data_collator=default_data_collator
if data_args.pad_to_max_length else None,
)
if data_args.do_prune:
logger.info("*** Purturbing mBART ***")
tasks = [data_args.task_name]
eval_datasets = [eval_dataset]
from ipdb import set_trace as bp
# bp()
model.prune_heads({layer: [head]})
# Training
if training_args.do_train:
train_result = trainer.train(
model_path=model_args.model_name_or_path if os.path.
isdir(model_args.model_name_or_path) else None)
metrics = train_result.metrics
trainer.save_model() # Saves the tokenizer too for easy upload
output_train_file = os.path.join(training_args.output_dir,
"train_results.txt")
if trainer.is_world_process_zero():
with open(output_train_file, "w") as writer:
logger.info("***** Train results *****")
for key, value in sorted(metrics.items()):
logger.info(f" {key} = {value}")
writer.write(f"{key} = {value}\n")
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(
os.path.join(training_args.output_dir,
"trainer_state.json"))
# Evaluation
eval_results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
# Loop to handle MNLI double evaluation (matched, mis-matched)
tasks = [data_args.task_name]
eval_datasets = [eval_dataset]
if data_args.task_name == "mnli":
tasks.append("mnli-mm")
eval_datasets.append(datasets["validation_mismatched"])
lang_head_specific_folder = os.path.join(
training_args.output_dir, data_args.language,
f"layer{layer}_head{head}")
if not os.path.exists(lang_head_specific_folder):
os.makedirs(lang_head_specific_folder)
for eval_dataset, task in zip(eval_datasets, tasks):
eval_result = trainer.evaluate(eval_dataset=eval_dataset)
output_eval_file = os.path.join(
lang_head_specific_folder, f"eval_results_{task}.txt")
if trainer.is_world_process_zero():
with open(output_eval_file, "w") as writer:
logger.info(f"***** Eval results {task} *****")
for key, value in sorted(eval_result.items()):
logger.info(f" {key} = {value}")
writer.write(f"{key} = {value}\n")
output_eval_file = os.path.join(
lang_head_specific_folder, f"eval_results_{task}.json")
if trainer.is_world_process_zero():
with open(output_eval_file, "w") as writer:
import json
json.dump(eval_result, writer)
eval_results.update(eval_result)
if training_args.do_predict:
logger.info("*** Test ***")
# Loop to handle MNLI double evaluation (matched, mis-matched)
tasks = [data_args.task_name]
test_datasets = [test_dataset]
if data_args.task_name == "mnli":
tasks.append("mnli-mm")
test_datasets.append(datasets["test_mismatched"])
for test_dataset, task in zip(test_datasets, tasks):
# Removing the `label` columns because it contains -1 and Trainer won't like that.
test_dataset.remove_columns_("label")
predictions = trainer.predict(
test_dataset=test_dataset).predictions
predictions = np.squeeze(
predictions) if is_regression else np.argmax(
predictions, axis=1)
output_test_file = os.path.join(
training_args.output_dir, f"test_results_{task}.txt")
if trainer.is_world_process_zero():
with open(output_test_file, "w") as writer:
logger.info(f"***** Test results {task} *****")
writer.write("index\tprediction\n")
for index, item in enumerate(predictions):
if is_regression:
writer.write(f"{index}\t{item:3.3f}\n")
else:
item = label_list[item]
writer.write(f"{index}\t{item}\n")
examples['n_real'] = [sum([0 if cls.tokenizer.convert_ids_to_tokens(i).startswith('##')
else 1 for i in line]) - 2 for line in examples['input_ids']]
return examples
if __name__ == '__main__':
from utils import get_tokenizer
from copy import deepcopy
t=get_tokenizer('bert-base-chinese', is_zh=True)
ds = get_tokenized_ds('hfds_scripts/atec_dataset.py', '../sentence-embedding/data/ATEC/atec_nlp_sim_train.csv', t, tokenize_type='with_prefix')
ds = ds['atec']
ds2=deepcopy(ds)
for index, ds_ in enumerate([ds, ds2]):
features=list(ds_.features)
for feature in features:
if index:
if feature.startswith('textb') or feature == 'label':
ds_.remove_columns_(feature)
else:
ds_.rename_column_(feature, feature[6:])
else:
if feature.startswith('texta') or feature == 'label':
ds_.remove_columns_(feature)
else:
ds_.rename_column_(feature, feature[6:])
ds=concatenate_datasets([ds, ds2])
print(ds)
