# Not able to predict using Transformers Trainer class

**URL:** <https://discuss.huggingface.co/t/not-able-to-predict-using-transformers-trainer-class/109710>\
**Category:** Intermediate\
**Created:** [October 1, 2024, 9:39pm UTC](https://discuss.huggingface.co/t/not-able-to-predict-using-transformers-trainer-class/109710 "2024-10-01T21:39:15Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![LijinDurairaj](https://avatars.discourse-cdn.com/v4/letter/l/8baadc/32.png) [@LijinDurairaj](https://discuss.huggingface.co/u/LijinDurairaj)\
**Post date:** [October 1, 2024, 9:39pm UTC](https://discuss.huggingface.co/t/not-able-to-predict-using-transformers-trainer-class/109710/1 "2024-10-01T21:39:15Z")

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I am learning to finetune a pre-trained model but when I am trying to test the finetuned model, I am getting this error

> IndexError: tuple index out of range

this is my [google colab notebook](https://colab.research.google.com/drive/1gRSPGiILNAh6_jCEqerq--g7Y06sYP-e?usp=sharing) and I am using this [Kaggle dataset](https://www.kaggle.com/datasets/snehaanbhawal/resume-dataset?resource=download)

while debugging I found some of the observations

1. when I pass the testing data with ‘input\_ids’,‘attention\_mask’ and ‘labels’, I am able to get the predicted results but when I am passing the data without labels ie. when I pass only ‘input\_ids’ and ‘attention\_mask’ I am getting the error.
2. but when I pass only ‘input\_ids’ and ‘attention\_mask’ of the training data, I am getting the predicted value as well which I am not able to understand

I am stuck and not able to move any further, any assistance to solve the issue would be appreciated

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<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [October 1, 2024, 10:28pm UTC](https://discuss.huggingface.co/t/not-able-to-predict-using-transformers-trainer-class/109710/2 "2024-10-01T22:28:38Z")

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The error itself is commonplace and often seen if you misspell the Python code, but after searching, it appears that it may be a long-term unresolved bug or bad specification.

> [@Error in fine-tuning BERT](https://discuss.huggingface.co/t/error-in-fine-tuning-bert/3469/8):
>
> I have a similar problem! I am working on a summarization model with Bart. so far I can train the model and it is fine, but when I want to do trainer.evaluate() it returns this warning: “”" VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify ‘dtype=object’ when creating the ndarray. result = getattr(asarray(obj), method)(…

> <https://github.com/huggingface/transformers/issues/11089>
>
> \## Environment info
> 
> \`transformers\` version: 4.3.3
> Platform: Linux-4.…19.112+-x86\_64-with-Ubuntu-18.04-bionic
> Python version: 3.7.10
> PyTorch version (GPU?): 1.7.1+cu101 (False)
> Tensorflow version (GPU?): 2.4.1 (False)
> Using GPU in script?: True/False
> Using distributed or parallel set-up in script?: False
> 
> 
> \### Who can help
> \- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
> \- tokenizers: @LysandreJik
> \- trainer: @sgugger
> \## Information
> 
> Model I am using (Bert, XLNet ...): \`LongFormer\`
> 
> 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:
> \* \[\] an official GLUE/SQUaD task: (give the name)
> \* \[x\] my own task or dataset: (give details below)
> 
> \## To reproduce
> 
> Hi, I am trying to train an LM model on a custom dataset (which is simply text over multiple lines). My choice was the Longformer, and I am using the exact same code provided officially with just a few modifications.
> 
> When I fine-tune it on a custom dataset, I am getting this error:-
> \`\`\`py
> \---------------------------------------------------------------------------
> 
> IndexError Traceback (most recent call last)
> 
> \<ipython-input-54-2f2d9c2c00fc\> in \<module\>()
> 45 )
> 46 
> \---\> 47 train\_results = trainer.train()
> 
> 6 frames
> 
> /usr/local/lib/python3.7/dist-packages/transformers/trainer.py in train(self, resume\_from\_checkpoint, trial, \*\*kwargs)
> 1032 self.control = self.callback\_handler.on\_epoch\_begin(self.args, self.state, self.control)
> 1033 
> \-\> 1034 for step, inputs in enumerate(epoch\_iterator):
> 1035 
> 1036 # Skip past any already trained steps if resuming training
> 
> /usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py in \_\_next\_\_(self)
> 515 if self.\_sampler\_iter is None:
> 516 self.\_reset()
> \--\> 517 data = self.\_next\_data()
> 518 self.\_num\_yielded += 1
> 519 if self.\_dataset\_kind == \_DatasetKind.Iterable and \\
> 
> /usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py in \_next\_data(self)
> 555 def \_next\_data(self):
> 556 index = self.\_next\_index() # may raise StopIteration
> \--\> 557 data = self.\_dataset\_fetcher.fetch(index) # may raise StopIteration
> 558 if self.\_pin\_memory:
> 559 data = \_utils.pin\_memory.pin\_memory(data)
> 
> /usr/local/lib/python3.7/dist-packages/torch/utils/data/\_utils/fetch.py in fetch(self, possibly\_batched\_index)
> 42 def fetch(self, possibly\_batched\_index):
> 43 if self.auto\_collation:
> \---\> 44 data = \[self.dataset\[idx\] for idx in possibly\_batched\_index\]
> 45 else:
> 46 data = self.dataset\[possibly\_batched\_index\]
> 
> /usr/local/lib/python3.7/dist-packages/torch/utils/data/\_utils/fetch.py in \<listcomp\>(.0)
> 42 def fetch(self, possibly\_batched\_index):
> 43 if self.auto\_collation:
> \---\> 44 data = \[self.dataset\[idx\] for idx in possibly\_batched\_index\]
> 45 else:
> 46 data = self.dataset\[possibly\_batched\_index\]
> 
> \<ipython-input-53-5e4959dcf50c\> in \_\_getitem\_\_(self, idx)
> 7 
> 8 def \_\_getitem\_\_(self, idx):
> \----\> 9 item = {key: torch.tensor(val\[idx\]) for key, val in self.encodings.items()}
> 10 item\['labels'\] = torch.tensor(self.labels\[idx\])
> 11 return item
> 
> \<ipython-input-53-5e4959dcf50c\> in \<dictcomp\>(.0)
> 7 
> 8 def \_\_getitem\_\_(self, idx):
> \----\> 9 item = {key: torch.tensor(val\[idx\]) for key, val in self.encodings.items()}
> 10 item\['labels'\] = torch.tensor(self.labels\[idx\])
> 11 return item
> 
> IndexError: list index out of range
> \`\`\`
> Most probably it is a tokenization problem, but I can't seem to locate it. 
> I ensured that the tokenizer in the \*\*LM\*\* does accept an appropriate length (even if it is quite bigger than I want):     
> 
> \`tokenizer = LongformerTokenizerFast.from\_pretrained("./ny\_model", max\_len=3500)\`
> 
> For fine-tuning, I ensured that it would truncate&pad, though none of my data samples are long enough to truncate:
> 
> \`\`\`py
> train\_encodings = tokenizer(list(train\_text), truncation=True, padding=True, max\_length=3500)
> val\_encodings = .....
> \`\`\`
> Finally, I tried with some dummy data with \_fixed length\_ like this:
> \`\`\`py
> train\_text = \['a', 'b'\]
> val\_text = \['c', 'd'\]
> \`\`\`
> Which rules out most tokenization errors. 
> I am fine-tuning in accordance to official scripts - something I have done before. the LM looks good to me and tokenizes individually as well, so I have no reason to suspect it. 
> 
> I am attaching my \*\*LM\*\* code:-
> \`\`\`py
> !pip install -q git+https://github.com/huggingface/transformers
> !pip list | grep -E 'transformers|tokenizers'
> 
> %%time 
> from pathlib import Path
> 
> from tokenizers import ByteLevelBPETokenizer
> 
> \# Initialize a tokenizer
> tokenizer = ByteLevelBPETokenizer()
> 
> \# Customize training
> tokenizer.train(files='./NYA.txt', vocab\_size=52\_000, min\_frequency=2, special\_tokens=\[
> "\<s\>",
> "\<pad\>",
> "\</s\>",
> "\<unk\>",
> "\<mask\>",
> \])
> 
> !mkdir ny\_model
> tokenizer.save\_model("ny\_model")
> 
> from transformers import LongformerConfig
> 
> config = LongformerConfig(
> vocab\_size=52\_000,
> max\_position\_embeddings=514,
> num\_attention\_heads=2,
> num\_hidden\_layers=1,
> type\_vocab\_size=1,
> )
> 
> from transformers import LongformerTokenizerFast
> 
> tokenizer = LongformerTokenizerFast.from\_pretrained("./ny\_model", max\_len=3500)
> 
> from transformers import LongformerForMaskedLM
> 
> model = LongformerForMaskedLM(config=config)
> 
> %%time
> from transformers import LineByLineTextDataset
> 
> dataset = LineByLineTextDataset(
> tokenizer=tokenizer,
> file\_path="./NYA.txt",
> block\_size=128,
> )
> from transformers import DataCollatorForLanguageModeling
> 
> data\_collator = DataCollatorForLanguageModeling(
> tokenizer=tokenizer, mlm=True, mlm\_probability=0.15
> )
> from transformers import Trainer, TrainingArguments
> 
> training\_args = TrainingArguments(
> output\_dir="./results",
> overwrite\_output\_dir=True,
> num\_train\_epochs=2,
> per\_device\_train\_batch\_size=64,
> save\_steps=10\_000,
> save\_total\_limit=2,
> prediction\_loss\_only=True,
> learning\_rate=1e-5,
> logging\_steps=50,
> fp16=True
> )
> 
> trainer = Trainer(
> model=model,
> args=training\_args,
> train\_dataset=dataset,
> data\_collator=data\_collator
> )
> trainer.train()
> \`\`\`
> and as said again, the fine-tuning part is again just like the official scripts, save the tokenizer arguments and some simple training args.
> 
> I believe that this code with a simple dummy dataset could reproduce the bug. I can provide further help on the gist if someone can create one for full reproducibility. If there is some idiotic mistake I have made, please don't hesitate to point that out.
> 
> \> Any Ideas what the problem might be?
> 
> Cheers

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<div class="post-metadata">

**Author:** ![samchain](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/samchain/32/30999_2.png) [@samchain](https://discuss.huggingface.co/u/samchain)\
**Post date:** [October 2, 2024, 9:07am UTC](https://discuss.huggingface.co/t/not-able-to-predict-using-transformers-trainer-class/109710/3 "2024-10-02T09:07:09Z")

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Hey,

If you use the ‘.predict’ method, then make sure to use the model within the Trainer. The Trainer itself is a wrapper around a model so maybe the predict in trainer is not the same that the predict in the model.

To check it you could :

```python
test_tokens = tokenizer("I am writing this post", return_tensors = "pt" ) #check format of the tensors
trainer.model.predict(test_tokens)

```

It might be something else but sometimes people get confused between the Trainer class and the model itself.