Evaluating QA model on single SQuAD file

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1

Hello guys

I would like to evaluate a model from the HF repo (‘mrm8488/bert-italian-finedtuned-squadv1-it-alfa’) on a SQuAD file I compiled, just to have a rough estimation of what could be the metrics.

This is the code i wrote:

from transformers import AutoTokenizer, AutoModelForQuestionAnswering, Trainer, TrainingArguments
import torch
from transformers import default_data_collator
import json

# Model from HuggingFace
model_checkpoint = 'mrm8488/bert-italian-finedtuned-squadv1-it-alfa'

# Import tokenizer
my_tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

# Import model
my_model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)

# Dataset for evaluation
eval_data_path = '/content/drive/MyDrive/BERT/SQuAD_files/result.json'

with open(eval_data_path) as json_file:
  data = json.load(json_file)

data_collator = default_data_collator
trainer = Trainer(
    my_model,
    data_collator=data_collator,
    tokenizer=my_tokenizer
)

trainer.evaluate(data)

Unfortunately, this does not work and I can’t understand why.
This is the error:

KeyError                                  Traceback (most recent call last)
<ipython-input-31-54109037e744> in <module>()
----> 1 trainer.evaluate(data)

5 frames
/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in evaluate(self, eval_dataset, ignore_keys, metric_key_prefix)
   2006             prediction_loss_only=True if self.compute_metrics is None else None,
   2007             ignore_keys=ignore_keys,
-> 2008             metric_key_prefix=metric_key_prefix,
   2009         )
   2010 

/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in evaluation_loop(self, dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix)
   2145         observed_num_examples = 0
   2146         # Main evaluation loop
-> 2147         for step, inputs in enumerate(dataloader):
   2148             # Update the observed num examples
   2149             observed_batch_size = find_batch_size(inputs)

/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]

KeyError: 0

A glimpse of my SQuAD formatted file:

{
  "data": [
    {
      "paragraphs": [
        {
          "qas": [
            {
              "question": "Qual è l’età?",
              "id": 78079,
              "answers": [
                {
                  "answer_id": 89658,
                  "document_id": 84480,
                  "question_id": 78079,
                  "text": "02/01/1966",
                  "answer_start": 113,
                  "answer_category": "SHORT"
                }
              ],
              "is_impossible": false
            },
            {
              "question": "Qual è il titolo di studio?",
              "id": 78082,
              "answers": [
                {
                  "answer_id": 89661,
                  "document_id": 84480,
                  "question_id": 78082,
                  "text": "media superiore",
                  "answer_start": 1157,
                  "answer_category": "SHORT"
                }
              ],
              "is_impossible": false
            },
    ...]
    "context" = "..."
    "document_id" = "..."

What am I doing wrong?
PS: is there a tool from the HF libraries to annotate files for question answering in a smart way?

Thanks a lot

2

The Trainer requires processed data. Have a look at the run_qa examples to see how it can be done,

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