 add one field  "example_id", but I can't see it in the "comput_loss" function
Hi ! Your function looks fine, I used to map `squad` locally and it indeed added the `example_id` field correctly.

However I think that in the `compute_loss` method only a subset of the fields are available: the model inputs. Since `example_id` is not a model input (it's not passed as a parameter to the model), the data loader doesn't need to return it by default.

However you can disable this behavior by setting `remove_unused_columns` to `False` to your training arguments. In this case in `compute_loss` you will get the full item with all the fields.

Note that since the model doesn't take `example_id` as input, you will have to remove it from the inputs when `model(**inputs)` is called Hi, I have set **args.remove_unused_columns=False** and  **training_args.remove_unused_columns=False**, but the field doesn't been contained yet.
```
def main():
    argp = HfArgumentParser(TrainingArguments)
    # The HfArgumentParser object collects command-line arguments into an object (and provides default values for unspecified arguments).
    # In particular, TrainingArguments has several keys that you'll need/want to specify (when you call run.py from the command line):
    # --do_train
    #     When included, this argument tells the script to train a model.
    #     See docstrings for "--task" and "--dataset" for how the training dataset is selected.
    # --do_eval
    #     When included, this argument tells the script to evaluate the trained/loaded model on the validation split of the selected dataset.
    # --per_device_train_batch_size <int, default=8>
    #     This is the training batch size.
    #     If you're running on GPU, you should try to make this as large as you can without getting CUDA out-of-memory errors.
    #     For reference, with --max_length=128 and the default ELECTRA-small model, a batch size of 32 should fit in 4gb of GPU memory.
    # --num_train_epochs <float, default=3.0>
    #     How many passes to do through the training data.
    # --output_dir <path>
    #     Where to put the trained model checkpoint(s) and any eval predictions.
    #     *This argument is required*.

    argp.add_argument('--model', type=str,
                      default='google/electra-small-discriminator',
                      help="""This argument specifies the base model to fine-tune.
        This should either be a HuggingFace model ID (see https://huggingface.co/models)
        or a path to a saved model checkpoint (a folder containing config.json and pytorch_model.bin).""")
    argp.add_argument('--task', type=str, choices=['nli', 'qa'], required=True,
                      help="""This argument specifies which task to train/evaluate on.
        Pass "nli" for natural language inference or "qa" for question answering.
        By default, "nli" will use the SNLI dataset, and "qa" will use the SQuAD dataset.""")
    argp.add_argument('--dataset', type=str, default=None,
                      help="""This argument overrides the default dataset used for the specified task.""")
    argp.add_argument('--max_length', type=int, default=128,
                      help="""This argument limits the maximum sequence length used during training/evaluation.
        Shorter sequence lengths need less memory and computation time, but some examples may end up getting truncated.""")
    argp.add_argument('--max_train_samples', type=int, default=None,
                      help='Limit the number of examples to train on.')
    argp.add_argument('--max_eval_samples', type=int, default=None,
                      help='Limit the number of examples to evaluate on.')

    argp.remove_unused_columns = False
    training_args, args = argp.parse_args_into_dataclasses()
    args.remove_unused_columns=False
    training_args.remove_unused_columns=False
```


```
**************** train_dataset: Dataset({
    features: ['id', 'title', 'context', 'question', 'answers'],
    num_rows: 87599
})


**************** train_dataset_featurized: Dataset({
    features: ['attention_mask', 'end_positions', 'input_ids', 'start_positions', 'token_type_ids'],
    num_rows: 87714
})
``` Hi, I print the value, all are set to False, but don't work.
```
********************* training_args: TrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_find_unused_parameters=None,
debug=[],
deepspeed=None,
disable_tqdm=False,
do_eval=False,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_steps=None,
evaluation_strategy=IntervalStrategy.NO,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
gradient_accumulation_steps=1,
greater_is_better=None,
group_by_length=False,
ignore_data_skip=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=5e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=-1,
log_level=-1,
log_level_replica=-1,
log_on_each_node=True,
logging_dir=./re_trained_model/runs/Dec01_14-15-08_399b9290604c,
logging_first_step=False,
logging_steps=500,
logging_strategy=IntervalStrategy.STEPS,
lr_scheduler_type=SchedulerType.LINEAR,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
output_dir=./re_trained_model,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=8,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=re_trained_model,
push_to_hub_organization=None,
push_to_hub_token=None,
remove_unused_columns=False,
report_to=['tensorboard'],
resume_from_checkpoint=None,
run_name=./re_trained_model,
save_on_each_node=False,
save_steps=500,
save_strategy=IntervalStrategy.STEPS,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_legacy_prediction_loop=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
)
```
```
********************* args: Namespace(dataset='squad', max_eval_samples=None, max_length=128, max_train_samples=None, model='google/electra-small-discriminator', remove_unused_columns=False, task='qa')
2021-12-01 14:15:10,048 - WARNING - datasets.builder - Reusing dataset squad (/root/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)
Some weights of the model checkpoint at google/electra-small-discriminator were not used when initializing ElectraForQuestionAnswering: ['discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias']
- This IS expected if you are initializing ElectraForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing ElectraForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of ElectraForQuestionAnswering were not initialized from the model checkpoint at google/electra-small-discriminator and are newly initialized: ['qa_outputs.bias', 'qa_outputs.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Preprocessing data... (this takes a little bit, should only happen once per dataset)
``` Hmmm, it might be because the default data collator removes all the fields with `string` type:

https://github.com/huggingface/transformers/blob/4c0dd199c8305903564c2edeae23d294edd4b321/src/transformers/data/data_collator.py#L107-L112

I guess you also need a custom data collator that doesn't remove them. can you give a tutorial about how to do this? I overwrite **get_train_dataloader**, and remove **_remove_unused_columns**, but it doesn't work.

```
    def get_train_dataloader(self) -> DataLoader:
        """
        Returns the training :class:`~torch.utils.data.DataLoader`.

        Will use no sampler if :obj:`self.train_dataset` does not implement :obj:`__len__`, a random sampler (adapted
        to distributed training if necessary) otherwise.

        Subclass and override this method if you want to inject some custom behavior.
        """
        if self.train_dataset is None:
            raise ValueError("Trainer: training requires a train_dataset.")

        train_dataset = self.train_dataset
        # if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):
        #     train_dataset = self._remove_unused_columns(train_dataset, description="training")

        if isinstance(train_dataset, torch.utils.data.IterableDataset):
            if self.args.world_size > 1:
                train_dataset = IterableDatasetShard(
                    train_dataset,
                    batch_size=self.args.train_batch_size,
                    drop_last=self.args.dataloader_drop_last,
                    num_processes=self.args.world_size,
                    process_index=self.args.process_index,
                )

            return DataLoader(
                train_dataset,
                batch_size=self.args.train_batch_size,
                collate_fn=self.data_collator,
                num_workers=self.args.dataloader_num_workers,
                pin_memory=self.args.dataloader_pin_memory,
            )

        train_sampler = self._get_train_sampler()

        return DataLoader(
            train_dataset,
            batch_size=self.args.train_batch_size,
            sampler=train_sampler,
            collate_fn=self.data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
            pin_memory=self.args.dataloader_pin_memory,
        )
``` Hi, it works now, thank you.
1. **args.remove_unused_columns=False** and **training_args.remove_unused_columns=False**
2. overwrite **get_train_dataloader**, and remove **_remove_unused_columns**
3. add new fields, and can be got in **inputs**. 
Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs

```
*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],
        [1, 1, 1,  ..., 0, 0, 0],
        [1, 1, 1,  ..., 0, 0, 0],
        ...,
        [1, 1, 1,  ..., 0, 0, 0],
        [1, 1, 1,  ..., 0, 0, 0],
        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],
        [ 101, 2054, 2515,  ...,    0,    0,    0],
        [ 101, 2054, 2106,  ...,    0,    0,    0],
        ...,
        [ 101, 2339, 2001,  ...,    0,    0,    0],
        [ 101, 2054, 2515,  ...,    0,    0,    0],
        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],
        [0, 0, 0,  ..., 0, 0, 0],
        [0, 0, 0,  ..., 0, 0, 0],
        ...,
        [0, 0, 0,  ..., 0, 0, 0],
        [0, 0, 0,  ..., 0, 0, 0],
        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} 
```

```
# This function preprocesses a question answering dataset, tokenizing the question and context text
# and finding the right offsets for the answer spans in the tokenized context (to use as labels).
# Adapted from https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py
def prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):
    questions = [q.lstrip() for q in examples["question"]]
    max_seq_length = tokenizer.model_max_length
    # tokenize both questions and the corresponding context
    # if the context length is longer than max_length, we split it to several
    # chunks of max_length
    tokenized_examples = tokenizer(
        questions,
        examples["context"],
        truncation="only_second",
        max_length=max_seq_length,
        stride=min(max_seq_length // 2, 128),
        return_overflowing_tokens=True,
        return_offsets_mapping=True,
        padding="max_length"
    )

    # Since one example might give us several features if it has a long context,
    # we need a map from a feature to its corresponding example.
    sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
    # The offset mappings will give us a map from token to character position
    # in the original context. This will help us compute the start_positions
    # and end_positions to get the final answer string.
    offset_mapping = tokenized_examples.pop("offset_mapping")

    tokenized_examples["start_positions"] = []
    tokenized_examples["end_positions"] = []

    tokenized_examples["example_id"] = []

    for i, offsets in enumerate(offset_mapping):
        input_ids = tokenized_examples["input_ids"][i]
        # We will label features not containing the answer the index of the CLS token.
        cls_index = input_ids.index(tokenizer.cls_token_id)
        sequence_ids = tokenized_examples.sequence_ids(i)
        # from the feature idx to sample idx
        sample_index = sample_mapping[i]
        # get the answer for a feature
        answers = examples["answers"][sample_index]

        tokenized_examples["example_id"].append(examples["id"][sample_index])

        if len(answers["answer_start"]) == 0:
            tokenized_examples["start_positions"].append(cls_index)
            tokenized_examples["end_positions"].append(cls_index)
        else:
            # Start/end character index of the answer in the text.
            start_char = answers["answer_start"][0]
            end_char = start_char + len(answers["text"][0])

            # Start token index of the current span in the text.
            token_start_index = 0
            while sequence_ids[token_start_index] != 1:
                token_start_index += 1

            # End token index of the current span in the text.
            token_end_index = len(input_ids) - 1
            while sequence_ids[token_end_index] != 1:
                token_end_index -= 1

            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
            if not (offsets[token_start_index][0] <= start_char and
                    offsets[token_end_index][1] >= end_char):
                tokenized_examples["start_positions"].append(cls_index)
                tokenized_examples["end_positions"].append(cls_index)
            else:
                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.
                # Note: we could go after the last offset if the answer is the last word (edge case).
                while token_start_index < len(offsets) and \
                        offsets[token_start_index][0] <= start_char:
                    token_start_index += 1
                tokenized_examples["start_positions"].append(
                    token_start_index - 1)
                while offsets[token_end_index][1] >= end_char:
                    token_end_index -= 1
                tokenized_examples["end_positions"].append(token_end_index + 1)

    return tokenized_examples
```

_Originally posted by @yanllearnn in https://github.com/huggingface/datasets/issues/3333#issuecomment-983457161_