Multiclass evaluation not working
Hello,

I am new to Transformers library and I'm trying to do Sequence Classification. I have 24 labels and I am getting the following error:

> ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

Even though I already added the averaging method as such:

`metric = load_metric('precision', average='weighted')`

Would someone kindly point me towards whatever I'm doing wrong? I'm able to fine-tune the pre-trained BERT model if I use 'accuracy' as the metric. But somehow it's not accepting my `average='weighted'` argument.
Hello! Could you provide more information, for example by filling the issue template for bugs, including the code sample and versions? Thanks.<|||||>No problem. Sorry, I wasn't sure if this was a bug or my own mistake so I didn't use the bug template. Here you go:

## 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: 4.17.0
- Platform: Linux-4.14.252-131.483.amzn1.x86_64-x86_64-with-glibc2.9
- Python version: 3.6.13
- PyTorch version (GPU?): 1.10.2+cu102 (False)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- 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, DeBERTa, DeBERTa-v2, ELECTRA, MobileBert, SqueezeBert: LysandreJik
- T5, Pegasus, EncoderDecoder: @patrickvonplaten
- Blenderbot, MBART, BART, Marian, Pegasus: @patil-suraj
- Reformer, TransfoXL, XLNet, FNet: @patrickvonplaten
- Longformer, BigBird: @ydshieh
- FSMT: @stas00
- Funnel: @sgugger
- GPT-2, GPT: @patil-suraj, @patrickvonplaten, @LysandreJik
- RAG, DPR: @patrickvonplaten, @lhoestq
- TensorFlow: @Rocketknight1
- JAX/Flax: @patil-suraj
- TAPAS, LayoutLM, LayoutLMv2, LUKE, ViT, BEiT, DEiT, DETR, CANINE: @NielsRogge
- GPT-Neo, GPT-J, CLIP: @patil-suraj
- Wav2Vec2, HuBERT, SpeechEncoderDecoder, UniSpeech, UniSpeechSAT, SEW, SEW-D, Speech2Text: @patrickvonplaten, @anton-l

If the model isn't in the list, ping @LysandreJik who will redirect you to the correct contributor.

Library:

- Benchmarks: @patrickvonplaten
- Deepspeed: @stas00
- Ray/raytune: @richardliaw, @amogkam
- Text generation: @patrickvonplaten @narsil
- Tokenizers: @SaulLu
- Trainer: @sgugger
- Pipelines: @Narsil
- Speech: @patrickvonplaten, @anton-l
- Vision: @NielsRogge, @sgugger

Documentation: @sgugger

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- for issues with a model, report at https://discuss.huggingface.co/ and tag the model's creator.

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
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- maintained examples (not research project or legacy): @sgugger, @patil-suraj

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1. @sgugger
2. @LysandreJik
3. @sgugger

## Information

Model I am using (Bert, XLNet ...):  bert-based-cased

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

Steps to reproduce the behavior:

1. Load own data set using `Dataset.from_pandas(my_data)`
2. Tokenize it with `AutoTokenizer.from_pretrained('bert-base-cased')`
3. Create training arguments, metric, and Trainer object to start training.

```
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=24)
training_args = TrainingArguments(output_dir='./checkpoints/my_model', evaluation_strategy="epoch")

metric = load_metric('precision', average='weighted')
def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    return metric.compute(predictions=predictions, references=labels)

trainer = Trainer(model=model,
                  args=training_args,
                  train_dataset=train_dataset,
                  eval_dataset=eval_dataset,
                  compute_metrics=compute_metrics)
trainer.train()
```
and I got this error:
```python
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-46-5aef28bcb00d> in <module>
     14                   eval_dataset=eval_dataset,
     15                   compute_metrics=compute_metrics)
---> 16 trainer.train()

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/transformers/trainer.py in train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   1488 
   1489             self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)
-> 1490             self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
   1491 
   1492             if DebugOption.TPU_METRICS_DEBUG in self.args.debug:

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/transformers/trainer.py in _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch, ignore_keys_for_eval)
   1600         metrics = None
   1601         if self.control.should_evaluate:
-> 1602             metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
   1603             self._report_to_hp_search(trial, epoch, metrics)
   1604 

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/transformers/trainer.py in evaluate(self, eval_dataset, ignore_keys, metric_key_prefix)
   2262             prediction_loss_only=True if self.compute_metrics is None else None,
   2263             ignore_keys=ignore_keys,
-> 2264             metric_key_prefix=metric_key_prefix,
   2265         )
   2266 

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/transformers/trainer.py in evaluation_loop(self, dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix)
   2503         # Metrics!
   2504         if self.compute_metrics is not None and all_preds is not None and all_labels is not None:
-> 2505             metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))
   2506         else:
   2507             metrics = {}

<ipython-input-46-5aef28bcb00d> in compute_metrics(eval_pred)
      7     logits, labels = eval_pred
      8     predictions = np.argmax(logits, axis=-1)
----> 9     return metric.compute(predictions=predictions, references=labels)
     10 
     11 trainer = Trainer(model=model,

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/metric.py in compute(self, predictions, references, **kwargs)
    428             inputs = {input_name: self.data[input_name] for input_name in self.features}
    429             with temp_seed(self.seed):
--> 430                 output = self._compute(**inputs, **compute_kwargs)
    431 
    432             if self.buf_writer is not None:

~/.cache/huggingface/modules/datasets_modules/metrics/precision/bfadb1cf35fe89242263de7dc028b248827c08ba075659c0e812d0fc6e5237c9/precision.py in _compute(self, predictions, references, labels, pos_label, average, sample_weight)
    116     def _compute(self, predictions, references, labels=None, pos_label=1, average="binary", sample_weight=None):
    117         score = precision_score(
--> 118             references, predictions, labels=labels, pos_label=pos_label, average=average, sample_weight=sample_weight
    119         )
    120         return {"precision": float(score) if score.size == 1 else score}

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/metrics/_classification.py in precision_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)
   1660                                                  warn_for=('precision',),
   1661                                                  sample_weight=sample_weight,
-> 1662                                                  zero_division=zero_division)
   1663     return p
   1664 

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-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)
   1463         raise ValueError("beta should be >=0 in the F-beta score")
   1464     labels = _check_set_wise_labels(y_true, y_pred, average, labels,
-> 1465                                     pos_label)
   1466 
   1467     # Calculate tp_sum, pred_sum, true_sum ###

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/sklearn/metrics/_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
   1294             raise ValueError("Target is %s but average='binary'. Please "
   1295                              "choose another average setting, one of %r."
-> 1296                              % (y_type, average_options))
   1297     elif pos_label not in (None, 1):
   1298         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'].
```

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## Expected behavior
I expect the model to complete training without an error. I am able to do this if I used  `metric = load_metric('accuracy')` but not with precision, recall, or f1.
<!-- A clear and concise description of what you would expect to happen. -->
<|||||>Same here<|||||>Hi there!

You are passing the ``average`` argument when you load the metric, instead, you should pass it to the `compute` method like this:

```
metric = load_metric('precision')
metric.compute(predictions=[0,1,2,3,4,4,4,4], references=[2,2,2,3,4,1,1,4], average="weighted")

Output:
----------------------
>>> {'precision': 0.625}
```

Hope this helps!<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.