Spaces:
Paused
Paused
#!/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 text classification.""" | |
# 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 | |
import warnings | |
from dataclasses import dataclass, field | |
from typing import List, Optional | |
import datasets | |
import evaluate | |
import numpy as np | |
from datasets import Value, load_dataset | |
import transformers | |
from transformers import ( | |
AutoConfig, | |
AutoModelForSequenceClassification, | |
AutoTokenizer, | |
DataCollatorWithPadding, | |
EvalPrediction, | |
HfArgumentParser, | |
Trainer, | |
TrainingArguments, | |
default_data_collator, | |
set_seed, | |
) | |
from transformers.trainer_utils import get_last_checkpoint | |
from transformers.utils import check_min_version, send_example_telemetry | |
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("4.34.0.dev0") | |
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") | |
logger = logging.getLogger(__name__) | |
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. | |
""" | |
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)."} | |
) | |
do_regression: bool = field( | |
default=None, | |
metadata={ | |
"help": "Whether to do regression instead of classification. If None, will be inferred from the dataset." | |
}, | |
) | |
text_column_names: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"The name of the text column in the input dataset or a CSV/JSON file." | |
'If not specified, will use the "sentence" column for single/multi-label classifcation task.' | |
) | |
}, | |
) | |
text_column_delimiter: Optional[str] = field( | |
default=" ", metadata={"help": "THe delimiter to use to join text columns into a single sentence."} | |
) | |
train_split_name: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": 'The name of the train split in the input dataset. If not specified, will use the "train" split when do_train is enabled' | |
}, | |
) | |
validation_split_name: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": 'The name of the validation split in the input dataset. If not specified, will use the "validation" split when do_eval is enabled' | |
}, | |
) | |
test_split_name: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": 'The name of the test split in the input dataset. If not specified, will use the "test" split when do_predict is enabled' | |
}, | |
) | |
remove_splits: Optional[str] = field( | |
default=None, | |
metadata={"help": "The splits to remove from the dataset. Multiple splits should be separated by commas."}, | |
) | |
remove_columns: Optional[str] = field( | |
default=None, | |
metadata={"help": "The columns to remove from the dataset. Multiple columns should be separated by commas."}, | |
) | |
label_column_name: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"The name of the label column in the input dataset or a CSV/JSON file." | |
'If not specified, will use the "label" column for single/multi-label classifcation task' | |
) | |
}, | |
) | |
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." | |
) | |
}, | |
) | |
shuffle_train_dataset: bool = field( | |
default=False, metadata={"help": "Whether to shuffle the train dataset or not."} | |
) | |
shuffle_seed: int = field( | |
default=42, metadata={"help": "Random seed that will be used to shuffle the train dataset."} | |
) | |
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." | |
) | |
}, | |
) | |
metric_name: Optional[str] = field(default=None, metadata={"help": "The metric to use for evaluation."}) | |
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.dataset_name is None: | |
if self.train_file is None or self.validation_file is None: | |
raise ValueError(" training/validation file or a dataset name.") | |
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`." | |
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)."}, | |
) | |
token: str = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " | |
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)." | |
) | |
}, | |
) | |
use_auth_token: bool = field( | |
default=None, | |
metadata={ | |
"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`." | |
}, | |
) | |
trust_remote_code: bool = field( | |
default=False, | |
metadata={ | |
"help": ( | |
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option" | |
"should only be set to `True` for repositories you trust and in which you have read the code, as it will" | |
"execute code present on the Hub on your local machine." | |
) | |
}, | |
) | |
ignore_mismatched_sizes: bool = field( | |
default=False, | |
metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}, | |
) | |
def get_label_list(raw_dataset, split="train") -> List[str]: | |
"""Get the list of labels from a mutli-label dataset""" | |
if isinstance(raw_dataset[split]["label"][0], list): | |
label_list = [label for sample in raw_dataset[split]["label"] for label in sample] | |
label_list = list(set(label_list)) | |
else: | |
label_list = raw_dataset[split].unique("label") | |
# we will treat the label list as a list of string instead of int, consistent with model.config.label2id | |
label_list = [str(label) for label in label_list] | |
return label_list | |
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 model_args.use_auth_token is not None: | |
warnings.warn("The `use_auth_token` argument is deprecated and will be removed in v4.34.", FutureWarning) | |
if model_args.token is not None: | |
raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.") | |
model_args.token = model_args.use_auth_token | |
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The | |
# information sent is the one passed as arguments along with your Python/PyTorch versions. | |
send_example_telemetry("run_classification", model_args, data_args) | |
# 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)], | |
) | |
if training_args.should_log: | |
# The default of training_args.log_level is passive, so we set log level at info here to have that default. | |
transformers.utils.logging.set_verbosity_info() | |
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: {training_args.parallel_mode.value == 'distributed'}, 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, or specify a dataset name | |
# to load from huggingface/datasets. In ether case, you can specify a the key of the column(s) containing the text and | |
# the key of the column containing the label. If multiple columns are specified for the text, they will be joined togather | |
# for the actual text value. | |
# In distributed training, the load_dataset function guarantee that only one local process can concurrently | |
# download the dataset. | |
if 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, | |
token=model_args.token, | |
) | |
# Try print some info about the dataset | |
logger.info(f"Dataset loaded: {raw_datasets}") | |
logger.info(raw_datasets) | |
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} | |
# Get the test dataset: you can provide your own CSV/JSON test file | |
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 dataset name 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, | |
token=model_args.token, | |
) | |
else: | |
# Loading a dataset from local json files | |
raw_datasets = load_dataset( | |
"json", | |
data_files=data_files, | |
cache_dir=model_args.cache_dir, | |
token=model_args.token, | |
) | |
# See more about loading any type of standard or custom dataset at | |
# https://huggingface.co/docs/datasets/loading_datasets.html. | |
if data_args.remove_splits is not None: | |
for split in data_args.remove_splits.split(","): | |
logger.info(f"removing split {split}") | |
raw_datasets.pop(split) | |
if data_args.train_split_name is not None: | |
logger.info(f"using {data_args.validation_split_name} as validation set") | |
raw_datasets["train"] = raw_datasets[data_args.train_split_name] | |
raw_datasets.pop(data_args.train_split_name) | |
if data_args.validation_split_name is not None: | |
logger.info(f"using {data_args.validation_split_name} as validation set") | |
raw_datasets["validation"] = raw_datasets[data_args.validation_split_name] | |
raw_datasets.pop(data_args.validation_split_name) | |
if data_args.test_split_name is not None: | |
logger.info(f"using {data_args.test_split_name} as test set") | |
raw_datasets["test"] = raw_datasets[data_args.test_split_name] | |
raw_datasets.pop(data_args.test_split_name) | |
if data_args.remove_columns is not None: | |
for split in raw_datasets.keys(): | |
for column in data_args.remove_columns.split(","): | |
logger.info(f"removing column {column} from split {split}") | |
raw_datasets[split].remove_columns(column) | |
if data_args.label_column_name is not None and data_args.label_column_name != "label": | |
for key in raw_datasets.keys(): | |
raw_datasets[key] = raw_datasets[key].rename_column(data_args.label_column_name, "label") | |
# 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 data_args.do_regression is None | |
else data_args.do_regression | |
) | |
is_multi_label = False | |
if is_regression: | |
label_list = None | |
num_labels = 1 | |
# regession requires float as label type, let's cast it if needed | |
for split in raw_datasets.keys(): | |
if raw_datasets[split].features["label"].dtype not in ["float32", "float64"]: | |
logger.warning( | |
f"Label type for {split} set to float32, was {raw_datasets[split].features['label'].dtype}" | |
) | |
features = raw_datasets[split].features | |
features.update({"label": Value("float32")}) | |
try: | |
raw_datasets[split] = raw_datasets[split].cast(features) | |
except TypeError as error: | |
logger.error( | |
f"Unable to cast {split} set to float32, please check the labels are correct, or maybe try with --do_regression=False" | |
) | |
raise error | |
else: # classification | |
if raw_datasets["train"].features["label"].dtype == "list": # multi-label classification | |
is_multi_label = True | |
logger.info("Label type is list, doing multi-label classification") | |
# Trying to find the number of labels in a multi-label classification task | |
# We have to deal with common cases that labels appear in the training set but not in the validation/test set. | |
# So we build the label list from the union of labels in train/val/test. | |
label_list = get_label_list(raw_datasets, split="train") | |
for split in ["validation", "test"]: | |
if split in raw_datasets: | |
val_or_test_labels = get_label_list(raw_datasets, split=split) | |
diff = set(val_or_test_labels).difference(set(label_list)) | |
if len(diff) > 0: | |
# add the labels that appear in val/test but not in train, throw a warning | |
logger.warning( | |
f"Labels {diff} in {split} set but not in training set, adding them to the label list" | |
) | |
label_list += list(diff) | |
# if label is -1, we throw a warning and remove it from the label list | |
for label in label_list: | |
if label == -1: | |
logger.warning("Label -1 found in label list, removing it.") | |
label_list.remove(label) | |
label_list.sort() | |
num_labels = len(label_list) | |
if num_labels <= 1: | |
raise ValueError("You need more than one label to do classification.") | |
# 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="text-classification", | |
cache_dir=model_args.cache_dir, | |
revision=model_args.model_revision, | |
token=model_args.token, | |
trust_remote_code=model_args.trust_remote_code, | |
) | |
if is_regression: | |
config.problem_type = "regression" | |
logger.info("setting problem type to regression") | |
elif is_multi_label: | |
config.problem_type = "multi_label_classification" | |
logger.info("setting problem type to multi label classification") | |
else: | |
config.problem_type = "single_label_classification" | |
logger.info("setting problem type to single label classification") | |
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, | |
token=model_args.token, | |
trust_remote_code=model_args.trust_remote_code, | |
) | |
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, | |
token=model_args.token, | |
trust_remote_code=model_args.trust_remote_code, | |
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes, | |
) | |
# 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 | |
# for training ,we will update the config with label infos, | |
# if do_train is not set, we will use the label infos in the config | |
if training_args.do_train and not is_regression: # classification, training | |
label_to_id = {v: i for i, v in enumerate(label_list)} | |
# update config with label infos | |
if model.config.label2id != label_to_id: | |
logger.warning( | |
"The label2id key in the model config.json is not equal to the label2id key of this " | |
"run. You can ignore this if you are doing finetuning." | |
) | |
model.config.label2id = label_to_id | |
model.config.id2label = {id: label for label, id in config.label2id.items()} | |
elif not is_regression: # classification, but not training | |
logger.info("using label infos in the model config") | |
logger.info("label2id: {}".format(model.config.label2id)) | |
label_to_id = model.config.label2id | |
else: # regression | |
label_to_id = None | |
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 multi_labels_to_ids(labels: List[str]) -> List[float]: | |
ids = [0.0] * len(label_to_id) # BCELoss requires float as target type | |
for label in labels: | |
ids[label_to_id[label]] = 1.0 | |
return ids | |
def preprocess_function(examples): | |
if data_args.text_column_names is not None: | |
text_column_names = data_args.text_column_names.split(",") | |
# join together text columns into "sentence" column | |
examples["sentence"] = examples[text_column_names[0]] | |
for column in text_column_names[1:]: | |
for i in range(len(examples[column])): | |
examples["sentence"][i] += data_args.text_column_delimiter + examples[column][i] | |
# Tokenize the texts | |
result = tokenizer(examples["sentence"], padding=padding, max_length=max_seq_length, truncation=True) | |
if label_to_id is not None and "label" in examples: | |
if is_multi_label: | |
result["label"] = [multi_labels_to_ids(l) for l in examples["label"]] | |
else: | |
result["label"] = [(label_to_id[str(l)] if l != -1 else -1) for l in examples["label"]] | |
return result | |
# Running the preprocessing pipeline on all the datasets | |
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.shuffle_train_dataset: | |
logger.info("Shuffling the training dataset") | |
train_dataset = train_dataset.shuffle(seed=data_args.shuffle_seed) | |
if data_args.max_train_samples is not None: | |
max_train_samples = min(len(train_dataset), data_args.max_train_samples) | |
train_dataset = train_dataset.select(range(max_train_samples)) | |
if training_args.do_eval: | |
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets: | |
if "test" not in raw_datasets and "test_matched" not in raw_datasets: | |
raise ValueError("--do_eval requires a validation or test dataset if validation is not defined.") | |
else: | |
logger.warning("Validation dataset not found. Falling back to test dataset for validation.") | |
eval_dataset = raw_datasets["test"] | |
else: | |
eval_dataset = raw_datasets["validation"] | |
if data_args.max_eval_samples is not None: | |
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) | |
eval_dataset = eval_dataset.select(range(max_eval_samples)) | |
if training_args.do_predict or data_args.test_file is not None: | |
if "test" not in raw_datasets: | |
raise ValueError("--do_predict requires a test dataset") | |
predict_dataset = raw_datasets["test"] | |
# remove label column if it exists | |
if data_args.max_predict_samples is not None: | |
max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples) | |
predict_dataset = predict_dataset.select(range(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]}.") | |
if data_args.metric_name is not None: | |
metric = ( | |
evaluate.load(data_args.metric_name, config_name="multilabel") | |
if is_multi_label | |
else evaluate.load(data_args.metric_name) | |
) | |
logger.info(f"Using metric {data_args.metric_name} for evaluation.") | |
else: | |
if is_regression: | |
metric = evaluate.load("mse") | |
logger.info("Using mean squared error (mse) as regression score, you can use --metric_name to overwrite.") | |
else: | |
if is_multi_label: | |
metric = evaluate.load("f1", config_name="multilabel") | |
logger.info( | |
"Using multilabel F1 for multi-label classification task, you can use --metric_name to overwrite." | |
) | |
else: | |
metric = evaluate.load("accuracy") | |
logger.info("Using accuracy as classification score, you can use --metric_name to overwrite.") | |
def compute_metrics(p: EvalPrediction): | |
preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions | |
if is_regression: | |
preds = np.squeeze(preds) | |
result = metric.compute(predictions=preds, references=p.label_ids) | |
elif is_multi_label: | |
preds = np.array([np.where(p > 0.5, 1, 0) for p in preds]) | |
# Micro F1 is commonly used in multi-label classification | |
result = metric.compute(predictions=preds, references=p.label_ids, average="micro") | |
else: | |
preds = np.argmax(preds, axis=1) | |
result = metric.compute(predictions=preds, references=p.label_ids) | |
if len(result) > 1: | |
result["combined_score"] = np.mean(list(result.values())).item() | |
return result | |
# Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, 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 ***") | |
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 ***") | |
# Removing the `label` columns if exists because it might contains -1 and Trainer won't like that. | |
if "label" in predict_dataset.features: | |
predict_dataset = predict_dataset.remove_columns("label") | |
predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions | |
if is_regression: | |
predictions = np.squeeze(predictions) | |
elif is_multi_label: | |
predictions = np.array([np.where(p > 0.5, 1, 0) for p in predictions]) | |
else: | |
predictions = np.argmax(predictions, axis=1) | |
output_predict_file = os.path.join(training_args.output_dir, "predict_results.txt") | |
if trainer.is_world_process_zero(): | |
with open(output_predict_file, "w") as writer: | |
logger.info("***** Predict results *****") | |
writer.write("index\tprediction\n") | |
for index, item in enumerate(predictions): | |
if is_regression: | |
writer.write(f"{index}\t{item:3.3f}\n") | |
elif is_multi_label: | |
# recover from multi-hot encoding | |
item = [label_list[i] for i in range(len(item)) if item[i] == 1] | |
writer.write(f"{index}\t{item}\n") | |
else: | |
item = label_list[item] | |
writer.write(f"{index}\t{item}\n") | |
logger.info("Predict results saved at {}".format(output_predict_file)) | |
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"} | |
if training_args.push_to_hub: | |
trainer.push_to_hub(**kwargs) | |
else: | |
trainer.create_model_card(**kwargs) | |
def _mp_fn(index): | |
# For xla_spawn (TPUs) | |
main() | |
if __name__ == "__main__": | |
main() | |