Update README.md

README.md

@@ -14,89 +14,89 @@ metrics:
 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)
+    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()
+    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()