t5 year
This commit is contained in:
Jakub Pokrywka
parent
fd0cae92e5
commit
7d028ae016
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import datetime
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from config import LABELS_DICT
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with open('../test-A/in.tsv','r') as f_in, open(f'../test-A/huggingface_format_year_as_text.csv', 'w') as f_hf:
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f_hf.write('text\tyear_cont\tdate\tday_of_year\tday_of_month\tmonth\tyear\tweekday\tlabel\n')
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for line_in in f_in:
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year_cont, date, text = line_in.rstrip('\n').split('\t')
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d = datetime.datetime.strptime(date,"%Y%m%d")
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day_of_year = str(d.timetuple().tm_yday)
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day_of_month = str(d.day)
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month = str(d.month)
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year = str(d.year)
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weekday = str(d.weekday())
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day_of_year = str(d.timetuple().tm_yday)
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text = 'year: ' + year + ' month: ' + month + ' day: ' + day_of_month + ' weekday: ' + weekday + ' ' + text
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f_hf.write(text +'\t' +year_cont +'\t'+ date + '\t' + day_of_year + '\t' + day_of_month + '\t' + month + '\t' + year + '\t' + weekday + '\t' + str('0') + '\n')
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for dataset in 'train', 'dev-0':
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with open(f'../{dataset}/in.tsv') as f_in, open(f'../{dataset}/expected.tsv') as f_exp, open(f'../{dataset}/huggingface_format_year_as_text.csv','w') as f_hf:
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f_hf.write('text\tyear_cont\tdate\tday_of_year\tday_of_month\tmonth\tyear\tweekday\tlabel\n')
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for line_in, line_exp in zip(f_in, f_exp):
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label = str(LABELS_DICT[line_exp.rstrip('\n')])
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year_cont,date,text = line_in.rstrip('\n').split('\t')
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d = datetime.datetime.strptime(date,"%Y%m%d")
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day_of_year = str(d.timetuple().tm_yday)
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day_of_month = str(d.day)
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month = str(d.month)
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year = str(d.year)
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weekday = str(d.weekday())
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day_of_year = str(d.timetuple().tm_yday)
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text = 'year: ' + year + ' month: ' + month + ' day: ' + day_of_month + ' weekday: ' + weekday + ' ' + text
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f_hf.write(text +'\t' +year_cont +'\t'+ date + '\t'+ day_of_year + '\t' + day_of_month + '\t' + month + '\t' + year + '\t' + weekday + '\t' + label + '\n')
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import pickle
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from transformers import AutoTokenizer, AutoModel, T5ForConditionalGeneration
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from tqdm import tqdm
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from config import LABELS_LIST
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device = 'cuda'
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model_path= 't5-retrained/checkpoint-110000'
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from transformers import AutoModelForSequenceClassification
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model = T5ForConditionalGeneration.from_pretrained(model_path).cuda()
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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for dataset in ('dev-0', 'test-A'):
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with open(f'../{dataset}/in.tsv') as f_in, open(f'../{dataset}/out.tsv','w') as f_out:
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for line_in in tqdm(f_in, total=150_000):
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_,_, text = line_in.split('\t')
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text = text.rstrip('\n')
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inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt").input_ids.to(device)
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outputs = model.generate(inputs)
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o = tokenizer.decode(outputs[0], skip_special_tokens=True)
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o = LABELS_LIST[int(o)]
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f_out.write(o + '\n')
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python run_summarization.py \
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--model_name_or_path google/t5-v1_1-base \
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--do_train \
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--train_file ../train/huggingface_format_year_as_text.csv \
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--validation_file ../dev-0/huggingface_format_year_as_text.csv \
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--source_prefix "classify: " \
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--summary_column 'label' \
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--max_target_length=4 \
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--max_source_length=64 \
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--num_train_epochs 20 \
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--output_dir ./t5-retrained \
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--per_device_train_batch_size=16 \
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--per_device_eval_batch_size=16 \
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--gradient_accumulation_steps=4 \
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--overwrite_output_dir \
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--predict_with_generate \
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--save_steps=10000 \
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--eval_steps=10000 \
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--evaluation_strategy steps
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@ -0,0 +1,622 @@
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 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 sequence to sequence.
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"""
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# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
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import logging
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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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import datasets
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import nltk # Here to have a nice missing dependency error message early on
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import numpy as np
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from datasets import load_dataset, load_metric
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import transformers
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from filelock import FileLock
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from transformers import (
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AutoConfig,
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AutoModelForSeq2SeqLM,
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AutoTokenizer,
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DataCollatorForSeq2Seq,
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HfArgumentParser,
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Seq2SeqTrainer,
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Seq2SeqTrainingArguments,
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set_seed,
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)
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from transformers.file_utils import is_offline_mode
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from transformers.trainer_utils import get_last_checkpoint
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from transformers.utils import check_min_version
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from transformers.utils.versions import require_version
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.11.0.dev0")
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require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
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logger = logging.getLogger(__name__)
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try:
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nltk.data.find("tokenizers/punkt")
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except (LookupError, OSError):
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if is_offline_mode():
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raise LookupError(
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"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
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)
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with FileLock(".lock") as lock:
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nltk.download("punkt", quiet=True)
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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 from.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
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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,
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metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
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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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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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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.
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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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text_column: Optional[str] = field(
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default=None,
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metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
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)
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summary_column: Optional[str] = field(
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default=None,
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metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
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)
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train_file: Optional[str] = field(
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default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."}
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)
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validation_file: Optional[str] = field(
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default=None,
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metadata={
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"help": "An optional input evaluation data file to evaluate the metrics (rouge) on "
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"(a jsonlines or csv file)."
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},
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)
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test_file: Optional[str] = field(
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default=None,
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metadata={
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"help": "An optional input test data file to evaluate the metrics (rouge) on " "(a jsonlines or csv file)."
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},
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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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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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max_source_length: Optional[int] = field(
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default=1024,
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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, sequences shorter will be padded."
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},
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)
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max_target_length: Optional[int] = field(
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default=128,
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metadata={
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"help": "The maximum total sequence length for target text after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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},
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)
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val_max_target_length: Optional[int] = field(
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default=None,
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metadata={
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"help": "The maximum total sequence length for validation target text after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
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"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
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"during ``evaluate`` and ``predict``."
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},
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)
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pad_to_max_length: bool = field(
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default=False,
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metadata={
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"help": "Whether to pad all samples to model maximum sentence length. "
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"If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
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"efficient on GPU but very bad for TPU."
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},
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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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},
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)
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max_predict_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
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"value if set."
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},
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)
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num_beams: Optional[int] = field(
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default=None,
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metadata={
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"help": "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
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"which is used during ``evaluate`` and ``predict``."
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},
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)
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ignore_pad_token_for_loss: bool = field(
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default=True,
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metadata={
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"help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
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},
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)
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source_prefix: Optional[str] = field(
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default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
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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"], "`train_file` should be a csv or a json 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"], "`validation_file` should be a csv or a json file."
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if self.val_max_target_length is None:
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self.val_max_target_length = self.max_target_length
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summarization_name_mapping = {
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"amazon_reviews_multi": ("review_body", "review_title"),
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"big_patent": ("description", "abstract"),
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"cnn_dailymail": ("article", "highlights"),
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"orange_sum": ("text", "summary"),
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"pn_summary": ("article", "summary"),
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"psc": ("extract_text", "summary_text"),
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"samsum": ("dialogue", "summary"),
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"thaisum": ("body", "summary"),
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"xglue": ("news_body", "news_title"),
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"xsum": ("document", "summary"),
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"wiki_summary": ("article", "highlights"),
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}
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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, Seq2SeqTrainingArguments))
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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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# 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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handlers=[logging.StreamHandler(sys.stdout)],
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)
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log_level = training_args.get_process_log_level()
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logger.setLevel(log_level)
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datasets.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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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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logger.info(f"Training/evaluation parameters {training_args}")
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if data_args.source_prefix is None and model_args.model_name_or_path in [
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"t5-small",
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"t5-base",
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"t5-large",
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"t5-3b",
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"t5-11b",
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]:
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logger.warning(
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"You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
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"`--source_prefix 'summarize: ' `"
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)
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# Detecting last checkpoint.
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last_checkpoint = None
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if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
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last_checkpoint = get_last_checkpoint(training_args.output_dir)
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if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
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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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elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
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logger.info(
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f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
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"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
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)
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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 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 first column for the full texts and the second column for the
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# summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
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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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raw_datasets = load_dataset(
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data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, sep = '\t',
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)
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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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extension = data_args.train_file.split(".")[-1]
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if data_args.validation_file is not None:
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data_files["validation"] = data_args.validation_file
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extension = data_args.validation_file.split(".")[-1]
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if data_args.test_file is not None:
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data_files["test"] = data_args.test_file
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extension = data_args.test_file.split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir, sep ='\t')
|
||||
# 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 = AutoConfig.from_pretrained(
|
||||
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
|
||||
cache_dir=model_args.cache_dir,
|
||||
revision=model_args.model_revision,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_fast=model_args.use_fast_tokenizer,
|
||||
revision=model_args.model_revision,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
model = AutoModelForSeq2SeqLM.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,
|
||||
)
|
||||
|
||||
model.resize_token_embeddings(len(tokenizer))
|
||||
|
||||
if model.config.decoder_start_token_id is None:
|
||||
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
|
||||
|
||||
prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
|
||||
|
||||
# Preprocessing the datasets.
|
||||
# We need to tokenize inputs and targets.
|
||||
if training_args.do_train:
|
||||
column_names = raw_datasets["train"].column_names
|
||||
elif training_args.do_eval:
|
||||
column_names = raw_datasets["validation"].column_names
|
||||
elif training_args.do_predict:
|
||||
column_names = raw_datasets["test"].column_names
|
||||
else:
|
||||
logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
|
||||
return
|
||||
|
||||
# Get the column names for input/target.
|
||||
dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None)
|
||||
if data_args.text_column is None:
|
||||
text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
|
||||
else:
|
||||
text_column = data_args.text_column
|
||||
if text_column not in column_names:
|
||||
raise ValueError(
|
||||
f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}"
|
||||
)
|
||||
if data_args.summary_column is None:
|
||||
summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
|
||||
else:
|
||||
summary_column = data_args.summary_column
|
||||
if summary_column not in column_names:
|
||||
raise ValueError(
|
||||
f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}"
|
||||
)
|
||||
|
||||
# Temporarily set max_target_length for training.
|
||||
max_target_length = data_args.max_target_length
|
||||
padding = "max_length" if data_args.pad_to_max_length else False
|
||||
|
||||
if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"):
|
||||
logger.warning(
|
||||
"label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for"
|
||||
f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
|
||||
)
|
||||
|
||||
def preprocess_function(examples):
|
||||
inputs = examples[text_column]
|
||||
targets = examples[summary_column]
|
||||
inputs = [prefix + inp for inp in inputs]
|
||||
model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)
|
||||
|
||||
# Setup the tokenizer for targets
|
||||
#import pdb; pdb.set_trace()
|
||||
targets = [str(a) for a in targets]
|
||||
with tokenizer.as_target_tokenizer():
|
||||
labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)
|
||||
|
||||
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
|
||||
# padding in the loss.
|
||||
if padding == "max_length" and data_args.ignore_pad_token_for_loss:
|
||||
labels["input_ids"] = [
|
||||
[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
|
||||
]
|
||||
|
||||
model_inputs["labels"] = labels["input_ids"]
|
||||
return model_inputs
|
||||
|
||||
if training_args.do_train:
|
||||
if "train" not in raw_datasets:
|
||||
raise ValueError("--do_train requires a train dataset")
|
||||
train_dataset = raw_datasets["train"]
|
||||
if data_args.max_train_samples is not None:
|
||||
train_dataset = train_dataset.select(range(data_args.max_train_samples))
|
||||
with training_args.main_process_first(desc="train dataset map pre-processing"):
|
||||
train_dataset = train_dataset.map(
|
||||
preprocess_function,
|
||||
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 train dataset",
|
||||
)
|
||||
|
||||
if training_args.do_eval:
|
||||
max_target_length = data_args.val_max_target_length
|
||||
if "validation" not in raw_datasets:
|
||||
raise ValueError("--do_eval requires a validation dataset")
|
||||
eval_dataset = raw_datasets["validation"]
|
||||
if data_args.max_eval_samples is not None:
|
||||
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
|
||||
with training_args.main_process_first(desc="validation dataset map pre-processing"):
|
||||
eval_dataset = eval_dataset.map(
|
||||
preprocess_function,
|
||||
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 validation dataset",
|
||||
)
|
||||
|
||||
if training_args.do_predict:
|
||||
max_target_length = data_args.val_max_target_length
|
||||
if "test" not in raw_datasets:
|
||||
raise ValueError("--do_predict requires a test dataset")
|
||||
predict_dataset = raw_datasets["test"]
|
||||
if data_args.max_predict_samples is not None:
|
||||
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
|
||||
with training_args.main_process_first(desc="prediction dataset map pre-processing"):
|
||||
predict_dataset = predict_dataset.map(
|
||||
preprocess_function,
|
||||
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 prediction dataset",
|
||||
)
|
||||
|
||||
# Data collator
|
||||
label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
|
||||
data_collator = DataCollatorForSeq2Seq(
|
||||
tokenizer,
|
||||
model=model,
|
||||
label_pad_token_id=label_pad_token_id,
|
||||
pad_to_multiple_of=8 if training_args.fp16 else None,
|
||||
)
|
||||
|
||||
# Metric
|
||||
metric = load_metric("rouge")
|
||||
|
||||
def postprocess_text(preds, labels):
|
||||
preds = [pred.strip() for pred in preds]
|
||||
labels = [label.strip() for label in labels]
|
||||
|
||||
# rougeLSum expects newline after each sentence
|
||||
preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
|
||||
labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
|
||||
|
||||
return preds, labels
|
||||
|
||||
def compute_metrics(eval_preds):
|
||||
preds, labels = eval_preds
|
||||
if isinstance(preds, tuple):
|
||||
preds = preds[0]
|
||||
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
|
||||
if data_args.ignore_pad_token_for_loss:
|
||||
# Replace -100 in the labels as we can't decode them.
|
||||
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
|
||||
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
|
||||
|
||||
# Some simple post-processing
|
||||
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
|
||||
|
||||
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
|
||||
# Extract a few results from ROUGE
|
||||
result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
|
||||
|
||||
prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
|
||||
result["gen_len"] = np.mean(prediction_lens)
|
||||
result = {k: round(v, 4) for k, v in result.items()}
|
||||
return result
|
||||
|
||||
# Initialize our Trainer
|
||||
trainer = Seq2SeqTrainer(
|
||||
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,
|
||||
compute_metrics=compute_metrics if training_args.predict_with_generate else None,
|
||||
)
|
||||
|
||||
# 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()
|
||||
|
||||
# Evaluation
|
||||
results = {}
|
||||
max_length = (
|
||||
training_args.generation_max_length
|
||||
if training_args.generation_max_length is not None
|
||||
else data_args.val_max_target_length
|
||||
)
|
||||
num_beams = data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
|
||||
if training_args.do_eval:
|
||||
logger.info("*** Evaluate ***")
|
||||
metrics = trainer.evaluate(max_length=max_length, num_beams=num_beams, metric_key_prefix="eval")
|
||||
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 ***")
|
||||
|
||||
predict_results = trainer.predict(
|
||||
predict_dataset, metric_key_prefix="predict", max_length=max_length, num_beams=num_beams
|
||||
)
|
||||
metrics = predict_results.metrics
|
||||
max_predict_samples = (
|
||||
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
|
||||
)
|
||||
metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
|
||||
|
||||
trainer.log_metrics("predict", metrics)
|
||||
trainer.save_metrics("predict", metrics)
|
||||
|
||||
if trainer.is_world_process_zero():
|
||||
if training_args.predict_with_generate:
|
||||
predictions = tokenizer.batch_decode(
|
||||
predict_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
|
||||
)
|
||||
predictions = [pred.strip() for pred in predictions]
|
||||
output_prediction_file = os.path.join(training_args.output_dir, "generated_predictions.txt")
|
||||
with open(output_prediction_file, "w") as writer:
|
||||
writer.write("\n".join(predictions))
|
||||
|
||||
if training_args.push_to_hub:
|
||||
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "summarization"}
|
||||
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)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def _mp_fn(index):
|
||||
# For xla_spawn (TPUs)
|
||||
main()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue