|
--- |
|
language: |
|
- en |
|
- zh |
|
tags: |
|
- translation |
|
license: apache-2.0 |
|
datasets: |
|
- DDDSSS/en-zh-dataset |
|
metrics: |
|
- bleu |
|
- sacrebleu |
|
--- |
|
该模型主要的训练数据是opus100和CodeAlpaca_20K中的英文作为翻译内容，采用chatglm作为翻译器翻译成中文，并将脏数据筛选后得到DDDSSS/en-zh-dataset数据集， |
|
缺点是这个模型的sentence len 较短，需要自己进行分句，要不然可能会出现，少翻或者不翻译的情况出现 |
|
!注意，如果是pretrain方法下载模型的话，可能部分参数会随机初始化，建议直接下载模型，并从本地读取。 |
|
|
|
|
|
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline |
|
parser.add_argument('--device', default="cpu", type=str, help='"cuda:1"、"cuda:2"……') |
|
mode_name = opt.model |
|
device = opt.device |
|
model = AutoModelForSeq2SeqLM.from_pretrained(mode_name) |
|
tokenizer = AutoTokenizer.from_pretrained(mode_name) |
|
translation = pipeline("translation_en_to_zh", model=model, tokenizer=tokenizer, |
|
torch_dtype="float", device_map=True,device=device) |
|
x=["If nothing is detected and there is a config.json file, it’s assumed the library is transformers.","By looking into the presence of files such as *.nemo or *saved_model.pb*, the Hub can determine if a model is from NeMo or Keras."] |
|
re = translation(x, max_length=450) |
|
print('翻译为：' ,re) |
|
|
|
|
|
微调： |
|
|
|
import numpy as np |
|
from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer |
|
import torch |
|
# books = load_from_disk("") |
|
books = load_dataset("json", data_files=".json") |
|
books = books["train"].train_test_split(test_size=0.2) |
|
checkpoint = "./opus-mt-en-zh" |
|
# checkpoint = "./model/checkpoint-19304" |
|
tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
|
source_lang = "en" |
|
target_lang = "zh" |
|
def preprocess_function(examples): |
|
inputs = [example[source_lang] for example in examples["translation"]] |
|
targets = [example[target_lang] for example in examples["translation"]] |
|
model_inputs = tokenizer(inputs, text_target=targets, max_length=512, truncation=True) |
|
return model_inputs |
|
tokenized_books = books.map(preprocess_function, batched=True) |
|
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint) |
|
metric = evaluate.load("sacrebleu") |
|
|
|
def postprocess_text(preds, labels): |
|
preds = [pred.strip() for pred in preds] |
|
labels = [[label.strip()] 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) |
|
|
|
labels = np.where(labels != -100, labels, tokenizer.pad_token_id) |
|
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) |
|
|
|
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) |
|
|
|
result = metric.compute(predictions=decoded_preds, references=decoded_labels) |
|
result = {"bleu": result["score"]} |
|
|
|
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 |
|
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) |
|
batchsize=4 |
|
training_args = Seq2SeqTrainingArguments( |
|
|
|
output_dir="./my_awesome_opus_books_model", |
|
evaluation_strategy="epoch", |
|
learning_rate=2e-4, |
|
per_device_train_batch_size=batchsize, |
|
per_device_eval_batch_size=batchsize, |
|
weight_decay=0.01, |
|
# save_total_limit=3, |
|
num_train_epochs=4, |
|
predict_with_generate=True, |
|
fp16=True, |
|
push_to_hub=False, |
|
save_strategy="epoch", |
|
jit_mode_eval=True |
|
) |
|
|
|
trainer = Seq2SeqTrainer( |
|
model=model, |
|
args=training_args, |
|
train_dataset=tokenized_books["train"], |
|
eval_dataset=tokenized_books["test"], |
|
tokenizer=tokenizer, |
|
data_collator=data_collator, |
|
compute_metrics=compute_metrics, |
|
) |
|
trainer.train() |
