Update README.md

README.md

@@ -10,4 +10,93 @@ datasets:
 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()