Trainer() doesnt print any output or eval during training
I am following the notebook to fine-tune roberta on MLM task:github.comhuggingface/notebooks/blob/main/examples/language_modeling.ipynb{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X4cRE8IbIrIV"
   },
   "source": [
    "If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "MOsHUjgdIrIW",This file has been truncated.show originalHowever, when I am running trainer.train(),the standard output of eval metrics during training is not showed.Also there is nothing in wandb.Any ideas?This is the code:def tokenize_function(examples):return tokenizer(examples[“text”])def group_texts(examples, block_size = 514):concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
    total_length = len(concatenated_examples[list(examples.keys())[0]])

    total_length = (total_length // block_size) * block_size

    result = {
            k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
            for k, t in concatenated_examples.items()
    }
    result["labels"] = result["input_ids"].copy()
    return resultdatasets = load_dataset(‘wikitext’, ‘wikitext-2-raw-v1’)model_checkpoint = “distilroberta-base”tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=[“text”])model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)lm_datasets = tokenized_datasets.map(group_texts,batched=True,batch_size=514,num_proc=4,load_from_cache_file=False)numBatch = 8numEpoch = 2numStep = tokenized_datasets.num_rows[‘train’] * numEpoch / numBatchWANDB_API_KEY = “…”out_dir = “/mlm1/”args = TrainingArguments(run_name=“robert_mlm1”,num_train_epochs=numEpoch,per_device_train_batch_size=numBatch,per_device_eval_batch_size=numBatch*4,gradient_accumulation_steps=2,learning_rate=2e-5,weight_decay=1e-2,warmup_ratio=1e-2,warmup_steps=int(numStep/100),save_total_limit=3,save_steps=200,logging_steps=4,eval_steps=4,do_eval=True,save_strategy=‘steps’,evaluation_strategy=‘steps’,output_dir= out_dir + “weights”,logging_dir= out_dir + “logs”,seed=23,report_to=‘wandb’,load_best_model_at_end=True)trainer = Trainer(model=model,args=args,train_dataset=lm_datasets["train"],
eval_dataset=lm_datasets["validation"],)trainer.train()