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
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()