# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
# Modifications copyright (C) 2022 IBM
# 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.
"""
Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) on a text file or a dataset.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=fill-mask
"""
# You can also adapt this script on your own masked language modeling task.
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional
import codecs
import re
from pathlib import Path
import numpy as np
import datasets
from datasets import load_dataset, load_metric
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
BertForMaskedLM,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.testing_utils import CaptureLogger
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
import torch
os.environ["WANDB_DISABLED"] = "true"
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.14.0")
require_version(
"datasets>=1.8.0",
"To fix: pip install -r examples/pytorch/language-modeling/requirements.txt",
)
logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
[docs]@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": "The model checkpoint for weights initialization."
"Don't set if you want to train a model from scratch."
},
)
model_type: Optional[str] = field(
default=None,
metadata={
"help": "If training from scratch, pass a model type from the list: "
+ ", ".join(MODEL_TYPES)
},
)
config_overrides: Optional[str] = field(
default=None,
metadata={
"help": "Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
},
)
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)."
},
)
freeze_token_embed: bool = field(
default=False,
metadata={"help": "Freeze the token embedding layer parameters"},
)
pretrained_token_embed: Optional[str] = field(
default=None,
metadata={
"help": "Initialize the token embedding layer with pre-trained token embeddings"
},
)
def __post_init__(self):
if self.config_overrides is not None and (
self.config_name is not None or self.model_name_or_path is not None
):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
)
[docs]@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
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)."
},
)
train_file: Optional[str] = field(
default=None, metadata={"help": "The input training data file (a text file)."}
)
validation_file: Optional[str] = field(
default=None,
metadata={
"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."
},
)
test_file: Optional[str] = field(
default=None,
metadata={
"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."
},
)
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_seq_length: Optional[int] = field(
default=512,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated."
},
)
line_by_line: bool = field(
default=False,
metadata={
"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."
},
)
pad_to_max_length: bool = field(
default=False,
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."
},
)
overwrite_cache: bool = field(
default=False,
metadata={"help": "Overwrite the cached training and evaluation sets"},
)
validation_split_percentage: Optional[int] = field(
default=5,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
keep_linebreaks: bool = field(
default=False,
metadata={"help": "Whether to keep line breaks when using TXT files or not."},
)
mlm_probability: float = field(
default=0.15,
metadata={"help": "Ratio of tokens to mask for masked language modeling loss"},
)
def __post_init__(self):
if (
self.dataset_name is None
and self.train_file is None
and self.validation_file is None
):
raise ValueError(
"Need either a dataset name or a training/validation file."
)
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in [
"csv",
"json",
"txt",
], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in [
"csv",
"json",
"txt",
], "`validation_file` should be a csv, a json or a txt file."
if self.test_file is not None:
extension = self.test_file.split(".")[-1]
assert extension in [
"csv",
"json",
"txt",
], "`test_file` should be a csv, a json or a txt file."
[docs]def read_txt_embeddings(file_name):
"""Load pre-trained word embeddings
Parameters
----------
filename : str
the path to the pre-trained word embedding file in Glove format
Returns
-------
wordEmbedding : numpy nd-array
Numpy array of embeddings
dictionary : dict
Dictionary of word to index mappings
"""
word2id = dict()
id2word = {}
wv = []
dimension = 0
with codecs.open(file_name, "r", "utf-8", errors="ignore") as f_in:
# read the file line by line
for line in f_in:
line = line.strip()
# if line is not empty
if line:
if len(line.split(" ")) == 2:
dimension = int(line.split(" ")[1])
else:
vocabulary = line.split(" ")[0]
# if we haven't already seen this word add it to dictionary and get the vectors
if vocabulary not in word2id:
if vocabulary.endswith("@@"):
vocabulary = vocabulary[:-2]
else:
vocabulary = vocabulary + "</w>"
word2id[vocabulary] = len(word2id)
id2word[word2id[vocabulary]] = vocabulary
if dimension != len(line.split(" ")[1:]):
print(line)
print(str(dimension) + "\t" + str(len(line.split(" ")[1:])))
exit()
temp = []
for i in line.split(" ")[1:]:
temp.append(float(i))
wv.append(temp)
# convert embedding list to numpy array
wv_np = np.array(wv)
return wv_np, word2id, id2word
[docs]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 training_args.overwrite_output_dir:
Path(training_args.output_dir).mkdir(parents=True, exist_ok=True)
# 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.warning(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.warning(
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/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).
#
# 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,
)
if "validation" not in raw_datasets.keys():
raw_datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
)
raw_datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
)
else:
data_files = {}
dataset_args = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
extension = (
data_args.train_file.split(".")[-1]
if data_args.train_file is not None
else data_args.validation_file.split(".")[-1]
)
if extension == "txt":
extension = "text"
dataset_args["keep_linebreaks"] = data_args.keep_linebreaks
raw_datasets = load_dataset(
extension,
data_files=data_files,
cache_dir=model_args.cache_dir,
**dataset_args,
)
# If no validation data is there, validation_split_percentage will be used to divide the dataset.
if "validation" not in raw_datasets.keys():
raw_datasets["validation"] = load_dataset(
extension,
data_files=data_files,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
**dataset_args,
)
raw_datasets["train"] = load_dataset(
extension,
data_files=data_files,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
**dataset_args,
)
# 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.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config_kwargs = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(
model_args.model_name_or_path, **config_kwargs
)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.config_overrides is not None:
logger.warning(f"Overriding config: {model_args.config_overrides}")
config.update_from_string(model_args.config_overrides)
logger.warning(f"New config: {config}")
tokenizer_kwargs = {
"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,
}
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name, **tokenizer_kwargs
)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, **tokenizer_kwargs
)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.model_name_or_path:
model = AutoModelForMaskedLM.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,
)
n_params = sum(
dict((p.data_ptr(), p.numel()) for p in model.parameters()).values()
)
logger.warning(f"Total parameters in the model ={n_params/2**20:.2f}M params")
if model_args.freeze_token_embed:
for name, param in model.named_parameters():
if "word_embedding" in name:
param.requires_grad = False
n_params = sum(
dict(
(p.data_ptr(), p.numel())
for p in model.parameters()
if p.requires_grad == True
).values()
)
logger.warning(
f"Training new model from scratch - Total size = {n_params/2**20:.2f}M params"
)
else:
model = AutoModelForMaskedLM.from_config(config)
n_params = sum(
dict((p.data_ptr(), p.numel()) for p in model.parameters()).values()
)
logger.warning(f"Total parameters in the model = {n_params/2**20:.2f}M params")
if model_args.freeze_token_embed:
for name, param in model.named_parameters():
if "word_embedding" in name:
param.requires_grad = False
n_params = sum(
dict(
(p.data_ptr(), p.numel())
for p in model.parameters()
if p.requires_grad == True
).values()
)
logger.warning(
f"Training new model from scratch : Total size = {n_params/2**20:.2f}M params"
)
model.resize_token_embeddings(len(tokenizer))
if model_args.freeze_token_embed:
assert model_args.pretrained_token_embed is not None
logger.warning(
"Reading pre-trained token embeddings from {0}".format(
model_args.pretrained_token_embed
)
)
embeddings, word2id, id2word = read_txt_embeddings(
model_args.pretrained_token_embed
)
param_name = ""
for name, param in model.named_parameters():
if "word_embedding" in name:
param_name = param
logger.warning(
"Embedding dimension is {0}".format(
model.bert.embeddings.word_embeddings.weight.size()
)
)
with torch.no_grad():
for every_word in word2id:
if len(tokenizer.encode([every_word], add_special_tokens=False)) != 1:
logger.error(
every_word
+ " is split into multiple sub-words instead of one "
+ str(tokenizer.encode([every_word], add_special_tokens=False))
)
exit()
pre_trained_embed_index = word2id[every_word]
bert_embedding_index = tokenizer.encode(
[every_word], add_special_tokens=False
)[0]
if isinstance(model, BertForMaskedLM):
model.bert.embeddings.word_embeddings.weight[
bert_embedding_index
] = torch.FloatTensor(embeddings[pre_trained_embed_index]).to(
model.bert.embeddings.word_embeddings.weight.device
)
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
column_names = raw_datasets["train"].column_names
else:
column_names = raw_datasets["validation"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
tok_logger = transformers.utils.logging.get_logger(
"transformers.tokenization_utils_base"
)
if data_args.max_seq_length is None:
max_seq_length = tokenizer.model_max_length
if max_seq_length > 512:
logger.warning(
f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
"Picking 512 instead. You can change that default value by passing --max_seq_length xxx."
)
max_seq_length = 512
else:
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)
if data_args.line_by_line:
# When using line_by_line, we just tokenize each nonempty line.
padding = "max_length" if data_args.pad_to_max_length else False
if tokenizer.pad_token is None:
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
config.pad_token_id = tokenizer.pad_token_id
model.resize_token_embeddings(len(tokenizer))
def tokenize_line_by_line(examples):
# Remove empty lines
examples[text_column_name] = [
re.sub("\s\s+", " ", line)
for line in examples[text_column_name]
if len(line.strip()) > 0
and (not line.isspace())
and (len(line.strip().split(" ")) > 1)
]
tokenized_dataset = tokenizer(
examples[text_column_name],
padding=padding,
truncation=True,
max_length=max_seq_length,
# We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it
# receives the `special_tokens_mask`.
return_special_tokens_mask=True,
)
return tokenized_dataset
with training_args.main_process_first(desc="dataset map tokenization"):
tokenized_datasets = raw_datasets.map(
tokenize_line_by_line,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[text_column_name],
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on dataset line_by_line",
)
else:
# Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
# We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
# efficient when it receives the `special_tokens_mask`.
def tokenize_function_block(examples):
return tokenizer(
examples[text_column_name], return_special_tokens_mask=True
)
with training_args.main_process_first(desc="dataset map tokenization"):
tokenized_datasets_pre_group = raw_datasets.map(
tokenize_function_block,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on every text in dataset",
)
# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
def group_texts_by_block(examples):
# Concatenate all texts.
concatenated_examples = {
k: list(chain(*examples[k])) for k in examples.keys()
}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
if total_length >= max_seq_length:
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [
t[i : i + max_seq_length]
for i in range(0, total_length, max_seq_length)
]
for k, t in concatenated_examples.items()
}
return result
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
# remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
# might be slower to preprocess.
#
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
with training_args.main_process_first(desc="grouping texts together"):
tokenized_datasets = tokenized_datasets_pre_group.map(
group_texts_by_block,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
desc=f"Grouping texts in chunks of {max_seq_length}",
)
if training_args.do_train:
if "train" not in tokenized_datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = tokenized_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 tokenized_datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = tokenized_datasets["validation"]
if data_args.max_eval_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
if data_args.test_file is not None:
test_dataset = tokenized_datasets["test"]
if data_args.max_eval_samples is not None:
test_dataset = test_dataset.select(range(data_args.max_eval_samples))
# Data collator
# This one will take care of randomly masking the tokens.
pad_to_multiple_of_8 = (
data_args.line_by_line
and training_args.fp16
and not data_args.pad_to_max_length
)
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm_probability=data_args.mlm_probability,
pad_to_multiple_of=8 if pad_to_multiple_of_8 else 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,
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)
trainer.save_model() # Saves the tokenizer too for easy upload
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.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
tokenizer.save_pretrained( training_args.output_dir )
# Evaluation
if training_args.do_eval:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
temp_model = AutoModelForMaskedLM.from_pretrained(last_checkpoint)
trainer.model.load_state_dict(temp_model.state_dict(), strict=True)
logger.warning("*** Evaluate ***")
metrics = trainer.evaluate()
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))
try:
perplexity = math.exp(metrics["eval_loss"])
except OverflowError:
perplexity = float("inf")
metrics["perplexity"] = perplexity
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
if data_args.test_file is not None:
logger.warning("*** Evaluate on test set***")
metrics = trainer.evaluate(eval_dataset=test_dataset, metric_key_prefix="test")
max_eval_samples = (
data_args.max_eval_samples
if data_args.max_eval_samples is not None
else len(test_dataset)
)
metrics["test_samples"] = min(max_eval_samples, len(test_dataset))
try:
perplexity = math.exp(metrics["test_loss"])
except OverflowError:
perplexity = float("inf")
metrics["perplexity"] = perplexity
trainer.log_metrics("test", metrics)
trainer.save_metrics("test", metrics)
if training_args.push_to_hub:
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "fill-mask"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:
kwargs["dataset_args"] = data_args.dataset_config_name
kwargs[
"dataset"
] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
else:
kwargs["dataset"] = data_args.dataset_name
trainer.push_to_hub(**kwargs)
else:
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "fill-mask"}
trainer.create_model_card(**kwargs)
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()