TypeError: __init__() got an unexpected keyword argument 'model' in `run_seq2seq.py` example when using on our own files
Author: amirveyseh
Pull request: no

## 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: 4.3.3
- Platform: Linux-4.15.0-109-generic-x86_64-with-debian-buster-sid
- Python version: 3.6.13
- PyTorch version (GPU?): 1.7.1 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No


### Who can help
<!-- Your issue will be replied to more quickly if you can figure out the right person to tag with @
 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- 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

 -->

@patil-suraj

## Information

I am using `run_seq2seq.py` in `transformers/examples/seq2seq`

The problem arises when using:
* the official example scripts: 
when I run the following:

```
python run_seq2seq.py \
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --task summarization \
    --train_file train.csv \
    --validation_file test.csv \
    --output_dir output \
    --overwrite_output_dir \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --predict_with_generate \
    --max_train_samples 500 \
    --max_val_samples 500
```

I get the following error:

```
Traceback (most recent call last):
  File "run_seq2seq.py", line 645, in <module>
    main()
  File "run_seq2seq.py", line 518, in main
    pad_to_multiple_of=8 if training_args.fp16 else None,
TypeError: __init__() got an unexpected keyword argument 'model'
```
The tasks I am working on is:
* my own task or dataset:

I take the examples provided in the README file for the custom CSV file. Specifically, I have two files `train.csv` and `test.csv` in the same directory as `run_seq2seq.py` with the following content:

```
text,summary
"I'm sitting here in a boring room. It's just another rainy Sunday afternoon. I'm wasting my time I got nothing to do. I'm hanging around I'm waiting for you. But nothing ever happens. And I wonder","I'm sitting in a room where I'm waiting for something to happen"
"I see trees so green, red roses too. I see them bloom for me and you. And I think to myself what a wonderful world. I see skies so blue and clouds so white. The bright blessed day, the dark sacred night. And I think to myself what a wonderful world.","I'm a gardener and I'm a big fan of flowers."
"Christmas time is here. Happiness and cheer. Fun for all that children call. Their favorite time of the year. Snowflakes in the air. Carols everywhere. Olden times and ancient rhymes. Of love and dreams to share","It's that time of year again."
```

## To reproduce

Steps to reproduce the behavior:

1. I copy and paste the file `run_seq2seq.py` located [here](https://github.com/huggingface/transformers/blob/master/examples/seq2seq/run_seq2seq.py) into a directory.
2. I create two files named `train.csv` and `test.csv` in the same directory the file `run_seq2seq.py` is located.
3. I run the command provided above.

Here's the full terminal output:

```
02/27/2021 21:10:36 - WARNING - __main__ -   Process rank: -1, device: cuda:0, n_gpu: 4distributed training: False, 16-bits training: False
02/27/2021 21:10:36 - INFO - __main__ -   Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='output', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluation_strategy=<EvaluationStrategy.NO: 'no'>, prediction_loss_only=False, per_device_train_batch_size=4, per_device_eval_batch_size=4, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=5e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, lr_scheduler_type=<SchedulerType.LINEAR: 'linear'>, warmup_steps=0, logging_dir='runs/Feb27_21-10-36_legendary1', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', fp16_backend='auto', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='output', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, ignore_data_skip=False, sharded_ddp=False, deepspeed=None, label_smoothing_factor=0.0, adafactor=False, group_by_length=False, report_to=['tensorboard'], ddp_find_unused_parameters=None, dataloader_pin_memory=True, sortish_sampler=False, predict_with_generate=True)
02/27/2021 21:10:36 - WARNING - datasets.builder -   Using custom data configuration default-40a1a8e44205ddce
Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/users/apouranb/.cache/huggingface/datasets/csv/default-40a1a8e44205ddce/0.0.0/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93...
Dataset csv downloaded and prepared to /home/users/apouranb/.cache/huggingface/datasets/csv/default-40a1a8e44205ddce/0.0.0/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93. Subsequent calls will reuse this data.
https://huggingface.co/t5-small/resolve/main/config.json not found in cache or force_download set to True, downloading to /home/users/apouranb/.cache/huggingface/transformers/tmpgh87jvjl
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.20k/1.20k [00:00<00:00, 843kB/s]
storing https://huggingface.co/t5-small/resolve/main/config.json in cache at /home/users/apouranb/.cache/huggingface/transformers/fe501e8fd6425b8ec93df37767fcce78ce626e34cc5edc859c662350cf712e41.406701565c0afd9899544c1cb8b93185a76f00b31e5ce7f6e18bbaef02241985
creating metadata file for /home/users/apouranb/.cache/huggingface/transformers/fe501e8fd6425b8ec93df37767fcce78ce626e34cc5edc859c662350cf712e41.406701565c0afd9899544c1cb8b93185a76f00b31e5ce7f6e18bbaef02241985
loading configuration file https://huggingface.co/t5-small/resolve/main/config.json from cache at /home/users/apouranb/.cache/huggingface/transformers/fe501e8fd6425b8ec93df37767fcce78ce626e34cc5edc859c662350cf712e41.406701565c0afd9899544c1cb8b93185a76f00b31e5ce7f6e18bbaef02241985
Model config T5Config {
  "architectures": [
    "T5WithLMHeadModel"
  ],
  "d_ff": 2048,
  "d_kv": 64,
  "d_model": 512,
  "decoder_start_token_id": 0,
  "dropout_rate": 0.1,
  "eos_token_id": 1,
  "feed_forward_proj": "relu",
  "initializer_factor": 1.0,
  "is_encoder_decoder": true,
  "layer_norm_epsilon": 1e-06,
  "model_type": "t5",
  "n_positions": 512,
  "num_decoder_layers": 6,
  "num_heads": 8,
  "num_layers": 6,
  "output_past": true,
  "pad_token_id": 0,
  "relative_attention_num_buckets": 32,
  "task_specific_params": {
    "summarization": {
      "early_stopping": true,
      "length_penalty": 2.0,
      "max_length": 200,
      "min_length": 30,
      "no_repeat_ngram_size": 3,
      "num_beams": 4,
      "prefix": "summarize: "
    },
    "translation_en_to_de": {
      "early_stopping": true,
      "max_length": 300,
      "num_beams": 4,
      "prefix": "translate English to German: "
    },
    "translation_en_to_fr": {
      "early_stopping": true,
      "max_length": 300,
      "num_beams": 4,
      "prefix": "translate English to French: "
    },
    "translation_en_to_ro": {
      "early_stopping": true,
      "max_length": 300,
      "num_beams": 4,
      "prefix": "translate English to Romanian: "
    }
  },
  "transformers_version": "4.3.3",
  "use_cache": true,
  "vocab_size": 32128
}

loading configuration file https://huggingface.co/t5-small/resolve/main/config.json from cache at /home/users/apouranb/.cache/huggingface/transformers/fe501e8fd6425b8ec93df37767fcce78ce626e34cc5edc859c662350cf712e41.406701565c0afd9899544c1cb8b93185a76f00b31e5ce7f6e18bbaef02241985
Model config T5Config {
  "architectures": [
    "T5WithLMHeadModel"
  ],
  "d_ff": 2048,
  "d_kv": 64,
  "d_model": 512,
  "decoder_start_token_id": 0,
  "dropout_rate": 0.1,
  "eos_token_id": 1,
  "feed_forward_proj": "relu",
  "initializer_factor": 1.0,
  "is_encoder_decoder": true,
  "layer_norm_epsilon": 1e-06,
  "model_type": "t5",
  "n_positions": 512,
  "num_decoder_layers": 6,
  "num_heads": 8,
  "num_layers": 6,
  "output_past": true,
  "pad_token_id": 0,
  "relative_attention_num_buckets": 32,
  "task_specific_params": {
    "summarization": {
      "early_stopping": true,
      "length_penalty": 2.0,
      "max_length": 200,
      "min_length": 30,
      "no_repeat_ngram_size": 3,
      "num_beams": 4,
      "prefix": "summarize: "
    },
    "translation_en_to_de": {
      "early_stopping": true,
      "max_length": 300,
      "num_beams": 4,
      "prefix": "translate English to German: "
    },
    "translation_en_to_fr": {
      "early_stopping": true,
      "max_length": 300,
      "num_beams": 4,
      "prefix": "translate English to French: "
    },
    "translation_en_to_ro": {
      "early_stopping": true,
      "max_length": 300,
      "num_beams": 4,
      "prefix": "translate English to Romanian: "
    }
  },
  "transformers_version": "4.3.3",
  "use_cache": true,
  "vocab_size": 32128
}

https://huggingface.co/t5-small/resolve/main/spiece.model not found in cache or force_download set to True, downloading to /home/users/apouranb/.cache/huggingface/transformers/tmpuwh13b51
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 792k/792k [00:00<00:00, 2.15MB/s]
storing https://huggingface.co/t5-small/resolve/main/spiece.model in cache at /home/users/apouranb/.cache/huggingface/transformers/65fc04e21f45f61430aea0c4fedffac16a4d20d78b8e6601d8d996ebefefecd2.3b69006860e7b5d0a63ffdddc01ddcd6b7c318a6f4fd793596552c741734c62d
creating metadata file for /home/users/apouranb/.cache/huggingface/transformers/65fc04e21f45f61430aea0c4fedffac16a4d20d78b8e6601d8d996ebefefecd2.3b69006860e7b5d0a63ffdddc01ddcd6b7c318a6f4fd793596552c741734c62d
https://huggingface.co/t5-small/resolve/main/tokenizer.json not found in cache or force_download set to True, downloading to /home/users/apouranb/.cache/huggingface/transformers/tmpt45yih6q
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.39M/1.39M [00:00<00:00, 3.50MB/s]
storing https://huggingface.co/t5-small/resolve/main/tokenizer.json in cache at /home/users/apouranb/.cache/huggingface/transformers/06779097c78e12f47ef67ecb728810c2ae757ee0a9efe9390c6419783d99382d.8627f1bd5d270a9fd2e5a51c8bec3223896587cc3cfe13edeabb0992ab43c529
creating metadata file for /home/users/apouranb/.cache/huggingface/transformers/06779097c78e12f47ef67ecb728810c2ae757ee0a9efe9390c6419783d99382d.8627f1bd5d270a9fd2e5a51c8bec3223896587cc3cfe13edeabb0992ab43c529
loading file https://huggingface.co/t5-small/resolve/main/spiece.model from cache at /home/users/apouranb/.cache/huggingface/transformers/65fc04e21f45f61430aea0c4fedffac16a4d20d78b8e6601d8d996ebefefecd2.3b69006860e7b5d0a63ffdddc01ddcd6b7c318a6f4fd793596552c741734c62d
loading file https://huggingface.co/t5-small/resolve/main/tokenizer.json from cache at /home/users/apouranb/.cache/huggingface/transformers/06779097c78e12f47ef67ecb728810c2ae757ee0a9efe9390c6419783d99382d.8627f1bd5d270a9fd2e5a51c8bec3223896587cc3cfe13edeabb0992ab43c529
https://huggingface.co/t5-small/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /home/users/apouranb/.cache/huggingface/transformers/tmpqjragsda
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 242M/242M [00:03<00:00, 73.1MB/s]
storing https://huggingface.co/t5-small/resolve/main/pytorch_model.bin in cache at /home/users/apouranb/.cache/huggingface/transformers/fee5a3a0ae379232608b6eed45d2d7a0d2966b9683728838412caccc41b4b0ed.ddacdc89ec88482db20c676f0861a336f3d0409f94748c209847b49529d73885
creating metadata file for /home/users/apouranb/.cache/huggingface/transformers/fee5a3a0ae379232608b6eed45d2d7a0d2966b9683728838412caccc41b4b0ed.ddacdc89ec88482db20c676f0861a336f3d0409f94748c209847b49529d73885
loading weights file https://huggingface.co/t5-small/resolve/main/pytorch_model.bin from cache at /home/users/apouranb/.cache/huggingface/transformers/fee5a3a0ae379232608b6eed45d2d7a0d2966b9683728838412caccc41b4b0ed.ddacdc89ec88482db20c676f0861a336f3d0409f94748c209847b49529d73885
All model checkpoint weights were used when initializing T5ForConditionalGeneration.

All the weights of T5ForConditionalGeneration were initialized from the model checkpoint at t5-small.
If your task is similar to the task the model of the checkpoint was trained on, you can already use T5ForConditionalGeneration for predictions without further training.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 161.24ba/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 383.81ba/s]
Traceback (most recent call last):
  File "run_seq2seq.py", line 645, in <module>
    main()
  File "run_seq2seq.py", line 518, in main
    pad_to_multiple_of=8 if training_args.fp16 else None,
TypeError: __init__() got an unexpected keyword argument 'model'
```

<!-- If you have code snippets, error messages, stack traces please provide them here as well.
     Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
     Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->

## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->


Comments:
I have the same exact problem. Even if you skip that part, it keeps happening in other parts of the code. It seems that file hasn't been updated with the last changes.

any solution for this? issue persists

This is because of the old version of `transformers`, upgrading to master should resolve this issue. Also always install transformers from source to use examples.

I had the same issue. Upgrading to master solved the issue. 

I have a somewhat related question: 
Currently, the metrics are logged at the end of the training.
The output right now looks like: 
```
***** train metrics *****
  epoch                      =     3.0
  init_mem_cpu_alloc_delta   =     7MB
  init_mem_cpu_peaked_delta  =     0MB
  init_mem_gpu_alloc_delta   =   230MB
  init_mem_gpu_peaked_delta  =     0MB
  train_mem_cpu_alloc_delta  =     0MB
  train_mem_cpu_peaked_delta =     1MB
  train_mem_gpu_alloc_delta  =   696MB
  train_mem_gpu_peaked_delta =  4220MB
  train_runtime              = 16.6234
  train_samples              =     100
  train_samples_per_second   =   2.346
02/28/2021 13:29:32 - INFO - __main__ -   *** Evaluate ***
***** Running Evaluation *****
  Num examples = 10
  Batch size = 8
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:02<00:00,  1.29s/it]
***** eval metrics *****
  epoch                     =     3.0
  eval_gen_len              =    61.9
  eval_loss                 =  3.8083
  eval_mem_cpu_alloc_delta  =     1MB
  eval_mem_cpu_peaked_delta =     0MB
  eval_mem_gpu_alloc_delta  =     0MB
  eval_mem_gpu_peaked_delta =   896MB
  eval_rouge1               = 11.4663
  eval_rouge2               =  1.0712
  eval_rougeL               =  9.2587
  eval_rougeLsum            =  9.6266
  eval_runtime              =  4.7684
  eval_samples              =      10
  eval_samples_per_second   =   2.097
```
I was wondering if  there an easier way of getting metrics (say rouge score/loss) for each epoch so that we can see how the training is going and plot the loss? One solution that I could think of was writing a custom callback function with `on_epoch_end`. Just wondering if there's an easier solution?


There is,
you could set the `evaluation_strategy`  and `logging_strategy` argument to `epoch`, which will tell the trainer to evaluate and log after each epoch. If it doesn't feel free to createe another issue.