Error when calculating glue score
State: closed | Author: D-i-l-r-u-k-s-h-i (NONE)

I was trying glue score along with other metrics here. But glue gives me this error;

```
import nlp
glue_metric = nlp.load_metric('glue',name="cola")

glue_score = glue_metric.compute(predictions, references)
```

```
---------------------------------------------------------------------------
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-8-b9210a524504> in <module>()
----> 1 glue_score = glue_metric.compute(predictions, references)

6 frames
/usr/local/lib/python3.6/dist-packages/nlp/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)
    191         """
    192         if predictions is not None:
--> 193             self.add_batch(predictions=predictions, references=references)
    194         self.finalize(timeout=timeout)
    195 

/usr/local/lib/python3.6/dist-packages/nlp/metric.py in add_batch(self, predictions, references, **kwargs)
    207         if self.writer is None:
    208             self._init_writer()
--> 209         self.writer.write_batch(batch)
    210 
    211     def add(self, prediction=None, reference=None, **kwargs):

/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
    155         if self.pa_writer is None:
    156             self._build_writer(pa_table=pa.Table.from_pydict(batch_examples))
--> 157         pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)
    158         if writer_batch_size is None:
    159             writer_batch_size = self.writer_batch_size

/usr/local/lib/python3.6/dist-packages/pyarrow/types.pxi in __iter__()

/usr/local/lib/python3.6/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray()

/usr/local/lib/python3.6/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()

/usr/local/lib/python3.6/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()

TypeError: an integer is required (got type str)
```
I'm not sure whether I'm doing this wrong or whether it's an issue. I would like to know a workaround. Thank you.

Comments: The glue metric for cola is a metric for classification. It expects label ids as integers as inputs.

I want to evaluate a sentence pair whether they are semantically equivalent, so I used MRPC and it gives the same error, does that mean we have to encode the sentences and parse as input?

using BertTokenizer;
```
encoded_reference=tokenizer.encode(reference, add_special_tokens=False)
encoded_prediction=tokenizer.encode(prediction, add_special_tokens=False)
```

`glue_score = glue_metric.compute(encoded_prediction, encoded_reference)`
```

ValueError                                Traceback (most recent call last)
<ipython-input-9-4c3a3ce7b583> in <module>()
----> 1 glue_score = glue_metric.compute(encoded_prediction, encoded_reference)

6 frames
/usr/local/lib/python3.6/dist-packages/nlp/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)
    198         predictions = self.data["predictions"]
    199         references = self.data["references"]
--> 200         output = self._compute(predictions=predictions, references=references, **metrics_kwargs)
    201         return output
    202 

/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in _compute(self, predictions, references)
    101             return pearson_and_spearman(predictions, references)
    102         elif self.config_name in ["mrpc", "qqp"]:
--> 103             return acc_and_f1(predictions, references)
    104         elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
    105             return {"accuracy": simple_accuracy(predictions, references)}

/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in acc_and_f1(preds, labels)
     60 def acc_and_f1(preds, labels):
     61     acc = simple_accuracy(preds, labels)
---> 62     f1 = f1_score(y_true=labels, y_pred=preds)
     63     return {
     64         "accuracy": acc,

/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)
   1097                        pos_label=pos_label, average=average,
   1098                        sample_weight=sample_weight,
-> 1099                        zero_division=zero_division)
   1100 
   1101 

/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)
   1224                                                  warn_for=('f-score',),
   1225                                                  sample_weight=sample_weight,
-> 1226                                                  zero_division=zero_division)
   1227     return f
   1228 

/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)
   1482         raise ValueError("beta should be >=0 in the F-beta score")
   1483     labels = _check_set_wise_labels(y_true, y_pred, average, labels,
-> 1484                                     pos_label)
   1485 
   1486     # Calculate tp_sum, pred_sum, true_sum ###

/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
   1314             raise ValueError("Target is %s but average='binary'. Please "
   1315                              "choose another average setting, one of %r."
-> 1316                              % (y_type, average_options))
   1317     elif pos_label not in (None, 1):
   1318         warnings.warn("Note that pos_label (set to %r) is ignored when "

ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

```

MRPC is also a binary classification task, so its metric is a binary classification metric.

To evaluate if pairs of sentences are semantically equivalent, maybe you could take a look at models that compute if one sentence entails the other or not (typically the kinds of model that could work well on the MRPC task).

Closing this one. Feel free to re-open if you have other questions :)