583 lines
26 KiB
Python
Executable File
583 lines
26 KiB
Python
Executable File
#!/usr/bin/env python
|
|
# coding=utf-8
|
|
# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# 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.
|
|
""" Finetuning the library models for sequence classification on GLUE."""
|
|
# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
|
|
|
|
import logging
|
|
import os
|
|
import random
|
|
import sys
|
|
from dataclasses import dataclass, field
|
|
from typing import Optional
|
|
|
|
import datasets
|
|
import numpy as np
|
|
from datasets import load_dataset, load_metric
|
|
|
|
import transformers
|
|
from transformers import (
|
|
AutoConfig,
|
|
AutoModelForSequenceClassification,
|
|
AutoTokenizer,
|
|
DataCollatorWithPadding,
|
|
EvalPrediction,
|
|
HfArgumentParser,
|
|
PretrainedConfig,
|
|
Trainer,
|
|
TrainingArguments,
|
|
default_data_collator,
|
|
set_seed,
|
|
)
|
|
from transformers.trainer_utils import get_last_checkpoint
|
|
from transformers.utils import check_min_version
|
|
from transformers.utils.versions import require_version
|
|
|
|
|
|
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
|
|
check_min_version("4.11.0.dev0")
|
|
|
|
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
|
|
|
|
task_to_keys = {
|
|
"cola": ("sentence", None),
|
|
"mnli": ("premise", "hypothesis"),
|
|
"mrpc": ("sentence1", "sentence2"),
|
|
"qnli": ("question", "sentence"),
|
|
"qqp": ("question1", "question2"),
|
|
"rte": ("sentence1", "sentence2"),
|
|
"sst2": ("sentence", None),
|
|
"stsb": ("sentence1", "sentence2"),
|
|
"wnli": ("sentence1", "sentence2"),
|
|
}
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@dataclass
|
|
class DataTrainingArguments:
|
|
"""
|
|
Arguments pertaining to what data we are going to input our model for training and eval.
|
|
|
|
Using `HfArgumentParser` we can turn this class
|
|
into argparse arguments to be able to specify them on
|
|
the command line.
|
|
"""
|
|
|
|
task_name: Optional[str] = field(
|
|
default=None,
|
|
metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())},
|
|
)
|
|
dataset_name: Optional[str] = field(
|
|
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
|
)
|
|
dataset_config_name: Optional[str] = field(
|
|
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
|
)
|
|
max_seq_length: int = field(
|
|
default=128,
|
|
metadata={
|
|
"help": "The maximum total input sequence length after tokenization. Sequences longer "
|
|
"than this will be truncated, sequences shorter will be padded."
|
|
},
|
|
)
|
|
overwrite_cache: bool = field(
|
|
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
|
|
)
|
|
pad_to_max_length: bool = field(
|
|
default=True,
|
|
metadata={
|
|
"help": "Whether to pad all samples to `max_seq_length`. "
|
|
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
|
|
},
|
|
)
|
|
max_train_samples: Optional[int] = field(
|
|
default=None,
|
|
metadata={
|
|
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
|
|
"value if set."
|
|
},
|
|
)
|
|
max_eval_samples: Optional[int] = field(
|
|
default=None,
|
|
metadata={
|
|
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
|
|
"value if set."
|
|
},
|
|
)
|
|
max_predict_samples: Optional[int] = field(
|
|
default=None,
|
|
metadata={
|
|
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
|
|
"value if set."
|
|
},
|
|
)
|
|
train_file: Optional[str] = field(
|
|
default=None, metadata={"help": "A csv or a json file containing the training data."}
|
|
)
|
|
validation_file: Optional[str] = field(
|
|
default=None, metadata={"help": "A csv or a json file containing the validation data."}
|
|
)
|
|
test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."})
|
|
|
|
def __post_init__(self):
|
|
if self.task_name is not None:
|
|
self.task_name = self.task_name.lower()
|
|
if self.task_name not in task_to_keys.keys():
|
|
raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys()))
|
|
elif self.dataset_name is not None:
|
|
pass
|
|
elif self.train_file is None or self.validation_file is None:
|
|
raise ValueError("Need either a GLUE task, a training/validation file or a dataset name.")
|
|
else:
|
|
train_extension = self.train_file.split(".")[-1]
|
|
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
|
|
validation_extension = self.validation_file.split(".")[-1]
|
|
assert (
|
|
validation_extension == train_extension
|
|
), "`validation_file` should have the same extension (csv or json) as `train_file`."
|
|
|
|
|
|
@dataclass
|
|
class ModelArguments:
|
|
"""
|
|
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
|
|
"""
|
|
|
|
model_name_or_path: str = field(
|
|
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
|
|
)
|
|
config_name: Optional[str] = field(
|
|
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
|
)
|
|
tokenizer_name: Optional[str] = field(
|
|
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
|
|
)
|
|
cache_dir: Optional[str] = field(
|
|
default=None,
|
|
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
|
|
)
|
|
use_fast_tokenizer: bool = field(
|
|
default=True,
|
|
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
|
|
)
|
|
model_revision: str = field(
|
|
default="main",
|
|
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
|
|
)
|
|
use_auth_token: bool = field(
|
|
default=False,
|
|
metadata={
|
|
"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
|
|
"with private models)."
|
|
},
|
|
)
|
|
|
|
|
|
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()
|
|
|
|
# Setup logging
|
|
logging.basicConfig(
|
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
|
handlers=[logging.StreamHandler(sys.stdout)],
|
|
)
|
|
|
|
log_level = training_args.get_process_log_level()
|
|
logger.setLevel(log_level)
|
|
datasets.utils.logging.set_verbosity(log_level)
|
|
transformers.utils.logging.set_verbosity(log_level)
|
|
transformers.utils.logging.enable_default_handler()
|
|
transformers.utils.logging.enable_explicit_format()
|
|
|
|
# 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}"
|
|
)
|
|
logger.info(f"Training/evaluation parameters {training_args}")
|
|
|
|
# Detecting last checkpoint.
|
|
last_checkpoint = None
|
|
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
|
|
last_checkpoint = get_last_checkpoint(training_args.output_dir)
|
|
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
|
|
raise ValueError(
|
|
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
|
|
"Use --overwrite_output_dir to overcome."
|
|
)
|
|
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
|
|
logger.info(
|
|
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
|
|
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
|
|
)
|
|
|
|
# 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.
|
|
raw_datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
|
|
elif data_args.dataset_name is not None:
|
|
# Downloading and loading a dataset from the hub.
|
|
raw_datasets = load_dataset(
|
|
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
|
)
|
|
else:
|
|
# Loading a dataset from your local files.
|
|
# CSV/JSON training and evaluation files are needed.
|
|
data_files = {"train": data_args.train_file, "validation": data_args.validation_file}
|
|
#data_files = {"train": data_args.train_file, "validation": data_args.validation_file, "test":data_args.test_file}
|
|
|
|
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
|
|
# when you use `do_predict` without specifying a GLUE benchmark task.
|
|
if training_args.do_predict:
|
|
if data_args.test_file is not None:
|
|
train_extension = data_args.train_file.split(".")[-1]
|
|
test_extension = data_args.test_file.split(".")[-1]
|
|
assert (
|
|
test_extension == train_extension
|
|
), "`test_file` should have the same extension (csv or json) as `train_file`."
|
|
data_files["test"] = data_args.test_file
|
|
else:
|
|
raise ValueError("Need either a GLUE task or a test file for `do_predict`.")
|
|
|
|
for key in data_files.keys():
|
|
logger.info(f"load a local file for {key}: {data_files[key]}")
|
|
|
|
if data_args.train_file.endswith(".csv"):
|
|
# Loading a dataset from local csv files
|
|
raw_datasets = load_dataset("csv", data_files=data_files, cache_dir=model_args.cache_dir, sep='\t')
|
|
else:
|
|
# Loading a dataset from local json files
|
|
raw_datasets = load_dataset("json", data_files=data_files, cache_dir=model_args.cache_dir)
|
|
# 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 = raw_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.
|
|
is_regression = raw_datasets["train"].features["label"].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 = raw_datasets["train"].unique("label")
|
|
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.
|
|
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,
|
|
revision=model_args.model_revision,
|
|
use_auth_token=True if model_args.use_auth_token else None,
|
|
)
|
|
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,
|
|
revision=model_args.model_revision,
|
|
use_auth_token=True if model_args.use_auth_token else None,
|
|
)
|
|
model = AutoModelForSequenceClassification.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,
|
|
revision=model_args.model_revision,
|
|
use_auth_token=True if model_args.use_auth_token else None,
|
|
)
|
|
|
|
# Preprocessing the raw_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.
|
|
non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
|
|
if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
|
|
sentence1_key, sentence2_key = "sentence1", "sentence2"
|
|
else:
|
|
if len(non_label_column_names) >= 2:
|
|
sentence1_key, sentence2_key = non_label_column_names[:2]
|
|
else:
|
|
sentence1_key, sentence2_key = non_label_column_names[0], None
|
|
|
|
# Padding strategy
|
|
if data_args.pad_to_max_length:
|
|
padding = "max_length"
|
|
else:
|
|
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
|
|
padding = False
|
|
|
|
# Some models have set the order of the labels to use, so let's make sure we do use it.
|
|
label_to_id = None
|
|
if (
|
|
model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
|
|
and data_args.task_name is not None
|
|
and not 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: int(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 and not is_regression:
|
|
label_to_id = {v: i for i, v in enumerate(label_list)}
|
|
|
|
if label_to_id is not None:
|
|
model.config.label2id = label_to_id
|
|
model.config.id2label = {id: label for label, id in config.label2id.items()}
|
|
elif data_args.task_name is not None and not is_regression:
|
|
model.config.label2id = {l: i for i, l in enumerate(label_list)}
|
|
model.config.id2label = {id: label for label, id in config.label2id.items()}
|
|
|
|
if data_args.max_seq_length > tokenizer.model_max_length:
|
|
logger.warning(
|
|
f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
|
|
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
|
|
)
|
|
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
|
|
|
|
def preprocess_function(examples):
|
|
# Tokenize the texts
|
|
args = (
|
|
(examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
|
|
)
|
|
result = tokenizer(examples['text'], padding=padding, max_length=max_seq_length, truncation=True)
|
|
|
|
# Map labels to IDs (not necessary for GLUE tasks)
|
|
if label_to_id is not None and "label" in examples:
|
|
result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]]
|
|
|
|
t = result
|
|
|
|
for column in 'date', 'day_of_month', 'day_of_year', 'month', 'year', 'weekday', 'year_cont':
|
|
if column == 'year':
|
|
#t[column] = [[examples[column][i] - 1995] * t.input_ids[i].index(2) + [0] *(len(b) - b.index(2)) for i in range(len(t.input_ids))]
|
|
t[column] = [[a-1995] * b.index(2) + [0] *(len(b) - b.index(2)) for a,b in zip(examples[column], t['input_ids'])]
|
|
else:
|
|
t[column] = [[a] * b.index(2) + [0] *(len(b) - b.index(2)) for a,b in zip(examples[column], t['input_ids'])]
|
|
#for i in range(len(t['input_ids'])):
|
|
# t['year'][i][1] = examples['year'][i] - 1995
|
|
# t['month'][i][2] = examples['month'][i]
|
|
# t['day_of_month'][i][3] = examples['day_of_month'][i]
|
|
# t['weekday'][i][4] = examples['weekday'][i]
|
|
|
|
return t
|
|
|
|
with training_args.main_process_first(desc="dataset map pre-processing"):
|
|
raw_datasets = raw_datasets.map(
|
|
preprocess_function,
|
|
batched=True,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
desc="Running tokenizer on dataset",
|
|
)
|
|
if training_args.do_train:
|
|
if "train" not in raw_datasets:
|
|
raise ValueError("--do_train requires a train dataset")
|
|
train_dataset = raw_datasets["train"]
|
|
if data_args.max_train_samples is not None:
|
|
train_dataset = train_dataset.select(range(data_args.max_train_samples))
|
|
|
|
if training_args.do_eval:
|
|
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
|
|
raise ValueError("--do_eval requires a validation dataset")
|
|
eval_dataset = raw_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
|
|
if data_args.max_eval_samples is not None:
|
|
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
|
|
|
|
if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:
|
|
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
|
|
raise ValueError("--do_predict requires a test dataset")
|
|
predict_dataset = raw_datasets["test_matched" if data_args.task_name == "mnli" else "test"]
|
|
if data_args.max_predict_samples is not None:
|
|
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
|
|
|
|
# Log a few random samples from the training set:
|
|
if training_args.do_train:
|
|
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)
|
|
else:
|
|
metric = load_metric("accuracy")
|
|
|
|
# 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):
|
|
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:
|
|
return {"mse": ((preds - p.label_ids) ** 2).mean().item()}
|
|
else:
|
|
return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}
|
|
|
|
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
|
|
if data_args.pad_to_max_length:
|
|
data_collator = default_data_collator
|
|
elif training_args.fp16:
|
|
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
|
|
else:
|
|
data_collator = None
|
|
|
|
# Initialize our Trainer
|
|
trainer = Trainer(
|
|
model=model,
|
|
args=training_args,
|
|
train_dataset=train_dataset if training_args.do_train else None,
|
|
eval_dataset=eval_dataset if training_args.do_eval else None,
|
|
compute_metrics=compute_metrics,
|
|
tokenizer=tokenizer,
|
|
data_collator=data_collator,
|
|
)
|
|
|
|
# Training
|
|
if training_args.do_train:
|
|
checkpoint = None
|
|
if training_args.resume_from_checkpoint is not None:
|
|
checkpoint = training_args.resume_from_checkpoint
|
|
elif last_checkpoint is not None:
|
|
checkpoint = last_checkpoint
|
|
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
|
metrics = train_result.metrics
|
|
max_train_samples = (
|
|
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
|
|
)
|
|
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
|
|
|
|
trainer.save_model() # Saves the tokenizer too for easy upload
|
|
|
|
trainer.log_metrics("train", metrics)
|
|
trainer.save_metrics("train", metrics)
|
|
trainer.save_state()
|
|
|
|
# Evaluation
|
|
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(raw_datasets["validation_mismatched"])
|
|
|
|
for eval_dataset, task in zip(eval_datasets, tasks):
|
|
metrics = trainer.evaluate(eval_dataset=eval_dataset)
|
|
|
|
max_eval_samples = (
|
|
data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
|
|
)
|
|
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
|
|
|
|
trainer.log_metrics("eval", metrics)
|
|
trainer.save_metrics("eval", metrics)
|
|
|
|
if training_args.do_predict:
|
|
logger.info("*** Predict ***")
|
|
|
|
# Loop to handle MNLI double evaluation (matched, mis-matched)
|
|
tasks = [data_args.task_name]
|
|
predict_datasets = [predict_dataset]
|
|
if data_args.task_name == "mnli":
|
|
tasks.append("mnli-mm")
|
|
predict_datasets.append(raw_datasets["test_mismatched"])
|
|
|
|
for predict_dataset, task in zip(predict_datasets, tasks):
|
|
# Removing the `label` columns because it contains -1 and Trainer won't like that.
|
|
predict_dataset = predict_dataset.remove_columns("label")
|
|
predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions
|
|
predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
|
|
|
|
output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt")
|
|
if trainer.is_world_process_zero():
|
|
with open(output_predict_file, "w") as writer:
|
|
logger.info(f"***** Predict 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")
|
|
|
|
if training_args.push_to_hub:
|
|
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
|
|
if data_args.task_name is not None:
|
|
kwargs["language"] = "en"
|
|
kwargs["dataset_tags"] = "glue"
|
|
kwargs["dataset_args"] = data_args.task_name
|
|
kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"
|
|
|
|
trainer.push_to_hub(**kwargs)
|
|
|
|
|
|
def _mp_fn(index):
|
|
# For xla_spawn (TPUs)
|
|
main()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|