NER pipeline doesn't work for a list of sequences
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: transformers==4.3.2
- Platform: Linux Ubuntu 20.04
- Python version:  3.6
- PyTorch version (GPU?): torch==1.7.0+cu101
- Tensorflow version (GPU?): tensorflow==2.4.1
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help
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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
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- gpt2: @patrickvonplaten, @LysandreJik
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- tensorflow: @jplu

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @n1t0, @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

HF projects:

- nlp datasets: [different repo](https://github.com/huggingface/nlp)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

 -->

Library:

- pipelines: @LysandreJik

Documentation: @sgugger

## Information

Model I am using (Bert, XLNet ...):

The problem arises when using:
* [x] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [x] an official GLUE/SQUaD task: (give the name)
* [ ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. i used the steps  [here](https://huggingface.co/transformers/task_summary.html#named-entity-recognition) to use pipelines for NER task with a little change, so my script is as follow:
```
from transformers import pipeline
nlp = pipeline("ner")
sequence = [
    "Hugging Face Inc. is a company based in New York City.",
    "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very close to the Manhattan Bridge which is visible from the window."
]
print(nlp(sequence))
```

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## Expected behavior
 i expected to get a list like this:
```
[
    [
        {'word': 'Hu', 'score': 0.999578595161438, 'entity': 'I-ORG', 'index': 1, 'start': 0, 'end': 2}
        {'word': '##gging', 'score': 0.9909763932228088, 'entity': 'I-ORG', 'index': 2, 'start': 2, 'end': 7}
        {'word': 'Face', 'score': 0.9982224702835083, 'entity': 'I-ORG', 'index': 3, 'start': 8, 'end': 12}
        {'word': 'Inc', 'score': 0.9994880557060242, 'entity': 'I-ORG', 'index': 4, 'start': 13, 'end': 16}
        {'word': 'New', 'score': 0.9994344711303711, 'entity': 'I-LOC', 'index': 11, 'start': 40, 'end': 43}
        {'word': 'York', 'score': 0.9993196129798889, 'entity': 'I-LOC', 'index': 12, 'start': 44, 'end': 48}
        {'word': 'City', 'score': 0.9993793964385986, 'entity': 'I-LOC', 'index': 13, 'start': 49, 'end': 53}
    ],
    [
        {'word': 'Hu', 'score': 0.9995632767677307, 'entity': 'I-ORG'},
        {'word': '##gging', 'score': 0.9915938973426819, 'entity': 'I-ORG'},
        {'word': 'Face', 'score': 0.9982671737670898, 'entity': 'I-ORG'},
        {'word': 'Inc', 'score': 0.9994403719902039, 'entity': 'I-ORG'},
        {'word': 'New', 'score': 0.9994346499443054, 'entity': 'I-LOC'},
        {'word': 'York', 'score': 0.9993270635604858, 'entity': 'I-LOC'},
        {'word': 'City', 'score': 0.9993864893913269, 'entity': 'I-LOC'},
        {'word': 'D', 'score': 0.9825621843338013, 'entity': 'I-LOC'},
        {'word': '##UM', 'score': 0.936983048915863, 'entity': 'I-LOC'},
        {'word': '##BO', 'score': 0.8987102508544922, 'entity': 'I-LOC'},
        {'word': 'Manhattan', 'score': 0.9758241176605225, 'entity': 'I-LOC'},
        {'word': 'Bridge', 'score': 0.990249514579773, 'entity': 'I-LOC'}
    ]
]
```
but i got this error

```
ValueError                                Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py in convert_to_tensors(self, tensor_type, prepend_batch_axis)
    770                 if not is_tensor(value):
--> 771                     tensor = as_tensor(value)
    772 

ValueError: expected sequence of length 16 at dim 1 (got 38)

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
6 frames
/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py in convert_to_tensors(self, tensor_type, prepend_batch_axis)
    786                     )
    787                 raise ValueError(
--> 788                     "Unable to create tensor, you should probably activate truncation and/or padding "
    789                     "with 'padding=True' 'truncation=True' to have batched tensors with the same length."
    790                 )

ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.

```

i know the problem is from tokenizer and i should use tokenizer with some arguments like this:

```
tokenizer(
    sequence,
    return_tensors="pt",
    truncation=True,
    padding=True,
    max_length=512,
)
```

but it's not clear from the documentation how can we define these argument("truncation=True", "padding=True", "max_length=512") when using pipelines for NER task

<!-- A clear and concise description of what you would expect to happen. -->

@Narsil, do you want to take a look at this?<|||||>Took a look, it seems the issue was not padding, but argument handling.

