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--- |
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language: |
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- en |
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- zh |
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tags: |
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- translation |
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license: apache-2.0 |
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datasets: |
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- DDDSSS/en-zh-dataset |
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metrics: |
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- bleu |
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- sacrebleu |
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--- |
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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 |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline |
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parser.add_argument('--device', default="cpu", type=str, help='"cuda:1"、"cuda:2"……') |
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mode_name = opt.model |
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device = opt.device |
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model = AutoModelForSeq2SeqLM.from_pretrained(mode_name) |
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tokenizer = AutoTokenizer.from_pretrained(mode_name) |
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translation = pipeline("translation_en_to_zh", model=model, tokenizer=tokenizer, |
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torch_dtype="float", device_map=True,device=device) |
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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."] |
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re = translation(x, max_length=450) |
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print('翻译为：' ,re) |
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微调： |
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import numpy as np |
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from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer |
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import torch |
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# books = load_from_disk("") |
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books = load_dataset("json", data_files=".json") |
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books = books["train"].train_test_split(test_size=0.2) |
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checkpoint = "./opus-mt-en-zh" |
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# checkpoint = "./model/checkpoint-19304" |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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source_lang = "en" |
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target_lang = "zh" |
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def preprocess_function(examples): |
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inputs = [example[source_lang] for example in examples["translation"]] |
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targets = [example[target_lang] for example in examples["translation"]] |
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model_inputs = tokenizer(inputs, text_target=targets, max_length=512, truncation=True) |
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return model_inputs |
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tokenized_books = books.map(preprocess_function, batched=True) |
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data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint) |
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metric = evaluate.load("sacrebleu") |
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def postprocess_text(preds, labels): |
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preds = [pred.strip() for pred in preds] |
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labels = [[label.strip()] for label in labels] |
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return preds, labels |
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def compute_metrics(eval_preds): |
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preds, labels = eval_preds |
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if isinstance(preds, tuple): |
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preds = preds[0] |
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decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) |
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labels = np.where(labels != -100, labels, tokenizer.pad_token_id) |
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decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) |
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decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) |
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result = metric.compute(predictions=decoded_preds, references=decoded_labels) |
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result = {"bleu": result["score"]} |
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prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds] |
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result["gen_len"] = np.mean(prediction_lens) |
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result = {k: round(v, 4) for k, v in result.items()} |
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return result |
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model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) |
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batchsize=4 |
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training_args = Seq2SeqTrainingArguments( |
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output_dir="./my_awesome_opus_books_model", |
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evaluation_strategy="epoch", |
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learning_rate=2e-4, |
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per_device_train_batch_size=batchsize, |
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per_device_eval_batch_size=batchsize, |
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weight_decay=0.01, |
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# save_total_limit=3, |
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num_train_epochs=4, |
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predict_with_generate=True, |
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fp16=True, |
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push_to_hub=False, |
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save_strategy="epoch", |
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jit_mode_eval=True |
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) |
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trainer = Seq2SeqTrainer( |
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model=model, |
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args=training_args, |
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train_dataset=tokenized_books["train"], |
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eval_dataset=tokenized_books["test"], |
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tokenizer=tokenizer, |
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data_collator=data_collator, |
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compute_metrics=compute_metrics, |
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) |
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trainer.train() |
