Add a template for examples and apply it for mlm and plm examples (#8153)
* Add a template for example scripts and apply it to mlm * Formatting * Fix test * Add plm script * Add a template for example scripts and apply it to mlm * Formatting * Fix test * Add plm script * Add a template for example scripts and apply it to mlm * Formatting * Fix test * Add plm script * Styling
This commit is contained in:
Sylvain Gugger
@@ -18,7 +18,7 @@ Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, .
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Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
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https://huggingface.co/models?filter=causal-lm
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"""
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# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
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# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
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import logging
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import math
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@@ -325,7 +325,7 @@ def main():
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perplexity = math.exp(eval_output["eval_loss"])
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results["perplexity"] = perplexity
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output_eval_file = os.path.join(training_args.output_dir, "eval_results_lm.txt")
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output_eval_file = os.path.join(training_args.output_dir, "eval_results_clm.txt")
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if trainer.is_world_process_zero():
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with open(output_eval_file, "w") as writer:
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logger.info("***** Eval results *****")
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310
examples/language-modeling/run_mlm.py
Normal file
310
examples/language-modeling/run_mlm.py
Normal file
@@ -0,0 +1,310 @@
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# coding=utf-8
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# Copyright 2020 The HuggingFace Team All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) on a text file or a dataset.
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Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
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https://huggingface.co/models?filter=masked-lm
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"""
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# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
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import logging
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import math
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import os
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import sys
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from dataclasses import dataclass, field
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from typing import Optional
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from datasets import load_dataset
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import transformers
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from transformers import (
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CONFIG_MAPPING,
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MODEL_FOR_MASKED_LM_MAPPING,
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AutoConfig,
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AutoModelForMaskedLM,
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AutoTokenizer,
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DataCollatorForLanguageModeling,
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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set_seed,
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)
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from transformers.trainer_utils import is_main_process
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logger = logging.getLogger(__name__)
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MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
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"""
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model_name_or_path: Optional[str] = field(
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default=None,
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metadata={
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"help": "The model checkpoint for weights initialization."
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"Don't set if you want to train a model from scratch."
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},
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)
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model_type: Optional[str] = field(
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default=None,
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metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
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)
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use_fast_tokenizer: bool = field(
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default=True,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
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)
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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dataset_name: Optional[str] = field(
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default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
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validation_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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max_seq_length: Optional[int] = field(
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default=None,
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metadata={
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"help": "The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated."
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},
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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mlm_probability: float = field(
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default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
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)
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def __post_init__(self):
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if self.dataset_name is None and self.train_file is None and self.validation_file is None:
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raise ValueError("Need either a dataset name or a training/validation file.")
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else:
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if self.train_file is not None:
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extension = self.train_file.split(".")[-1]
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assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
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if self.validation_file is not None:
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extension = self.validation_file.split(".")[-1]
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assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
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def main():
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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if (
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os.path.exists(training_args.output_dir)
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and os.listdir(training_args.output_dir)
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and training_args.do_train
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and not training_args.overwrite_output_dir
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):
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty."
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"Use --overwrite_output_dir to overcome."
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)
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# Setup logging
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO if is_main_process(training_args.local_rank) else logging.WARN,
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)
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# Log on each process the small summary:
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logger.warning(
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f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
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+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
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)
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# Set the verbosity to info of the Transformers logger (on main process only):
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if is_main_process(training_args.local_rank):
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transformers.utils.logging.set_verbosity_info()
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logger.info("Training/evaluation parameters %s", training_args)
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# Set seed before initializing model.
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set_seed(training_args.seed)
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# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
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# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
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# (the dataset will be downloaded automatically from the datasets Hub
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#
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# For CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this
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# behavior (see below)
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#
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# In distributed training, the load_dataset function guarantee that only one local process can concurrently
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# download the dataset.
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if data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
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else:
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data_files = {}
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if data_args.train_file is not None:
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data_files["train"] = data_args.train_file
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if data_args.validation_file is not None:
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data_files["validation"] = data_args.train_file
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extension = data_args.train_file.split(".")[-1]
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if extension == "txt":
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extension = "text"
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datasets = load_dataset(extension, data_files=data_files)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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# Load pretrained model and tokenizer
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#
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# Distributed training:
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# The .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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if model_args.config_name:
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config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
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elif model_args.model_name_or_path:
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config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
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else:
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config = CONFIG_MAPPING[model_args.model_type]()
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logger.warning("You are instantiating a new config instance from scratch.")
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if model_args.tokenizer_name:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
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)
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elif model_args.model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
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)
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else:
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raise ValueError(
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"You are instantiating a new tokenizer from scratch. This is not supported by this script."
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"You can do it from another script, save it, and load it from here, using --tokenizer_name."
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)
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if model_args.model_name_or_path:
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model = AutoModelForMaskedLM.from_pretrained(
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model_args.model_name_or_path,
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from_tf=bool(".ckpt" in model_args.model_name_or_path),
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config=config,
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cache_dir=model_args.cache_dir,
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)
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else:
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logger.info("Training new model from scratch")
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model = AutoModelForMaskedLM.from_config(config)
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model.resize_token_embeddings(len(tokenizer))
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# Preprocessing the datasets.
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# First we tokenize all the texts.
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if training_args.do_train:
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column_names = datasets["train"].column_names
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else:
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column_names = datasets["validation"].column_names
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text_column_name = "text" if "text" in column_names else column_names[0]
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def tokenize_function(examples):
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# Remove empty lines
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examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
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return tokenizer(examples["text"], truncation=True, max_length=data_args.max_seq_length)
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tokenized_datasets = datasets.map(
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tokenize_function,
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batched=True,
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num_proc=data_args.preprocessing_num_workers,
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remove_columns=[text_column_name],
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load_from_cache_file=not data_args.overwrite_cache,
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)
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# Data collator
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# This one will take care of randomly masking the tokens.
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data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
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# Initialize our Trainer
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=tokenized_datasets["train"] if training_args.do_train else None,
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eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None,
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tokenizer=tokenizer,
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data_collator=data_collator,
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)
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# Training
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if training_args.do_train:
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trainer.train(
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model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
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)
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trainer.save_model() # Saves the tokenizer too for easy upload
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# Evaluation
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results = {}
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if training_args.do_eval:
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logger.info("*** Evaluate ***")
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eval_output = trainer.evaluate()
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perplexity = math.exp(eval_output["eval_loss"])
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results["perplexity"] = perplexity
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output_eval_file = os.path.join(training_args.output_dir, "eval_results_mlm.txt")
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if trainer.is_world_process_zero():
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with open(output_eval_file, "w") as writer:
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logger.info("***** Eval results *****")
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for key, value in results.items():
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logger.info(f" {key} = {value}")
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writer.write(f"{key} = {value}\n")
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return results
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def _mp_fn(index):
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# For xla_spawn (TPUs)
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main()
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if __name__ == "__main__":
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main()
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310
examples/language-modeling/run_plm.py
Normal file
310
examples/language-modeling/run_plm.py
Normal file
@@ -0,0 +1,310 @@
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# coding=utf-8
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# Copyright 2020 The HuggingFace Team All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
|
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# You may obtain a copy of the License at
|
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#
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# http://www.apache.org/licenses/LICENSE-2.0
|
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#
|
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# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
|
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# limitations under the License.
|
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"""
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Fine-tuning the library models for permutation language modeling.
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"""
|
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# You can also adapt this script on your own permutation language modeling task. Pointers for this are left as comments.
|
||||
|
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import logging
|
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import math
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import os
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import sys
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from dataclasses import dataclass, field
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from typing import Optional
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from datasets import load_dataset
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import transformers
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from transformers import (
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AutoConfig,
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AutoTokenizer,
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DataCollatorForPermutationLanguageModeling,
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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XLNetConfig,
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XLNetLMHeadModel,
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set_seed,
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)
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from transformers.trainer_utils import is_main_process
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logger = logging.getLogger(__name__)
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
|
||||
"""
|
||||
|
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model_name_or_path: Optional[str] = field(
|
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default=None,
|
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metadata={
|
||||
"help": "The model checkpoint for weights initialization."
|
||||
"Don't set if you want to train a model from scratch."
|
||||
},
|
||||
)
|
||||
config_name: Optional[str] = field(
|
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
|
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
|
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default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
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)
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use_fast_tokenizer: bool = field(
|
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default=True,
|
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
|
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)
|
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|
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|
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@dataclass
|
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class DataTrainingArguments:
|
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"""
|
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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)."},
|
||||
)
|
||||
overwrite_cache: bool = field(
|
||||
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
||||
)
|
||||
max_seq_length: Optional[int] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "The maximum total input sequence length after tokenization. Sequences longer "
|
||||
"than this will be truncated."
|
||||
},
|
||||
)
|
||||
preprocessing_num_workers: Optional[int] = field(
|
||||
default=None,
|
||||
metadata={"help": "The number of processes to use for the preprocessing."},
|
||||
)
|
||||
plm_probability: float = field(
|
||||
default=1 / 6,
|
||||
metadata={
|
||||
"help": "Ratio of length of a span of masked tokens to surrounding context length for "
|
||||
"permutation language modeling."
|
||||
},
|
||||
)
|
||||
max_span_length: int = field(
|
||||
default=5, metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}
|
||||
)
|
||||
|
||||
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."
|
||||
|
||||
|
||||
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."
|
||||
)
|
||||
|
||||
# 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()
|
||||
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. 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.
|
||||
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
|
||||
else:
|
||||
data_files = {}
|
||||
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.train_file
|
||||
extension = data_args.train_file.split(".")[-1]
|
||||
if extension == "txt":
|
||||
extension = "text"
|
||||
datasets = load_dataset(extension, data_files=data_files)
|
||||
# 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.
|
||||
if model_args.config_name:
|
||||
config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
|
||||
elif model_args.model_name_or_path:
|
||||
config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
|
||||
else:
|
||||
config = XLNetConfig()
|
||||
logger.warning("You are instantiating a new config instance from scratch.")
|
||||
|
||||
if model_args.tokenizer_name:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
||||
)
|
||||
elif model_args.model_name_or_path:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
||||
)
|
||||
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 = XLNetLMHeadModel.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,
|
||||
)
|
||||
else:
|
||||
logger.info("Training new model from scratch")
|
||||
model = XLNetLMHeadModel.from_config(config)
|
||||
|
||||
model.resize_token_embeddings(len(tokenizer))
|
||||
|
||||
# Preprocessing the datasets.
|
||||
# First we tokenize all the texts.
|
||||
if training_args.do_train:
|
||||
column_names = datasets["train"].column_names
|
||||
else:
|
||||
column_names = datasets["validation"].column_names
|
||||
text_column_name = "text" if "text" in column_names else column_names[0]
|
||||
|
||||
def tokenize_function(examples):
|
||||
# Remove empty lines
|
||||
examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
|
||||
return tokenizer(examples["text"], truncation=True, max_length=data_args.max_seq_length)
|
||||
|
||||
tokenized_datasets = datasets.map(
|
||||
tokenize_function,
|
||||
batched=True,
|
||||
num_proc=data_args.preprocessing_num_workers,
|
||||
remove_columns=[text_column_name],
|
||||
load_from_cache_file=not data_args.overwrite_cache,
|
||||
)
|
||||
|
||||
# Data collator
|
||||
data_collator = DataCollatorForPermutationLanguageModeling(
|
||||
tokenizer=tokenizer,
|
||||
plm_probability=data_args.plm_probability,
|
||||
max_span_length=data_args.max_span_length,
|
||||
)
|
||||
|
||||
# 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,
|
||||
)
|
||||
|
||||
# Training
|
||||
if training_args.do_train:
|
||||
trainer.train(
|
||||
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
|
||||
)
|
||||
trainer.save_model() # Saves the tokenizer too for easy upload
|
||||
|
||||
# Evaluation
|
||||
results = {}
|
||||
if training_args.do_eval:
|
||||
logger.info("*** Evaluate ***")
|
||||
|
||||
eval_output = trainer.evaluate()
|
||||
|
||||
perplexity = math.exp(eval_output["eval_loss"])
|
||||
results["perplexity"] = perplexity
|
||||
|
||||
output_eval_file = os.path.join(training_args.output_dir, "eval_results_plm.txt")
|
||||
if trainer.is_world_process_zero():
|
||||
with open(output_eval_file, "w") as writer:
|
||||
logger.info("***** Eval results *****")
|
||||
for key, value in results.items():
|
||||
logger.info(f" {key} = {value}")
|
||||
writer.write(f"{key} = {value}\n")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def _mp_fn(index):
|
||||
# For xla_spawn (TPUs)
|
||||
main()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -37,7 +37,7 @@ if SRC_DIRS is not None:
|
||||
import run_clm
|
||||
import run_generation
|
||||
import run_glue
|
||||
import run_language_modeling
|
||||
import run_mlm
|
||||
import run_pl_glue
|
||||
import run_squad
|
||||
|
||||
@@ -160,31 +160,29 @@ class ExamplesTests(TestCasePlus):
|
||||
result = run_clm.main()
|
||||
self.assertLess(result["perplexity"], 100)
|
||||
|
||||
def test_run_language_modeling(self):
|
||||
def test_run_mlm(self):
|
||||
stream_handler = logging.StreamHandler(sys.stdout)
|
||||
logger.addHandler(stream_handler)
|
||||
|
||||
tmp_dir = self.get_auto_remove_tmp_dir()
|
||||
testargs = f"""
|
||||
run_language_modeling.py
|
||||
run_mlm.py
|
||||
--model_name_or_path distilroberta-base
|
||||
--model_type roberta
|
||||
--mlm
|
||||
--line_by_line
|
||||
--train_data_file ./tests/fixtures/sample_text.txt
|
||||
--eval_data_file ./tests/fixtures/sample_text.txt
|
||||
--train_file ./tests/fixtures/sample_text.txt
|
||||
--validation_file ./tests/fixtures/sample_text.txt
|
||||
--output_dir {tmp_dir}
|
||||
--overwrite_output_dir
|
||||
--do_train
|
||||
--do_eval
|
||||
--prediction_loss_only
|
||||
--num_train_epochs=1
|
||||
""".split()
|
||||
""".split()
|
||||
|
||||
if torch_device != "cuda":
|
||||
testargs.append("--no_cuda")
|
||||
|
||||
with patch.object(sys, "argv", testargs):
|
||||
result = run_language_modeling.main()
|
||||
result = run_mlm.main()
|
||||
self.assertLess(result["perplexity"], 42)
|
||||
|
||||
def test_run_squad(self):
|
||||
|
||||
Reference in New Issue
Block a user