# Dataset Object without ClassLabel

**URL:** <https://discuss.huggingface.co/t/dataset-object-without-classlabel/33029>\
**Category:** 🤗Datasets\
**Created:** [March 4, 2023, 4:24pm UTC](https://discuss.huggingface.co/t/dataset-object-without-classlabel/33029 "2023-03-04T16:24:07Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![uygarkurt](https://avatars.discourse-cdn.com/v4/letter/u/f04885/32.png) [@uygarkurt](https://discuss.huggingface.co/u/uygarkurt)\
**Post date:** [March 4, 2023, 4:24pm UTC](https://discuss.huggingface.co/t/dataset-object-without-classlabel/33029/1 "2023-03-04T16:24:07Z")

</div>

Hi everyone. I’m working on a sequence labelling task. During the training f1 scores are abnormally high for validation set. When I try inference it barely gets anythihg right. I thought it may be about how I used the dataset. I don’t get any errors by the way.

I create the dataset with `from_list()` function as follows:

```auto
train_dataset = Dataset.from_list(train_l)
valid_dataset = Dataset.from_list(valid_l)
test_dataset = Dataset.from_list(test_l)

```

where each element of train/test/val\_l is dictionary in the following form:  
`{"tokens": [...], "tags":[...]}`

After this I simply tokenize each dataset and align tags with the new tokens. When I run  
`print(tokenized_train.features)`  
Outputs:  
`{'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),`  
`'tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None),`  
`'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None),`  
`'token_type_ids': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), `  
`'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), `  
`'labels': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}`

Where `labels` are the labels including special `-100` and aligned with the byte-pair tokenization. Tags are simplly the tags used for tokens without alighment.

I think there’s something wrong in here. Documentation sasy these should be `ClassLabel` so that trainer can understand which values are the target values. On the other hand when I check documentation on loading datasets from local machine it doesn’t mention anything about it.

Can you spot anything wrong here? Should I set `ClassLabel` or do anything like that?

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<div class="post-metadata">

**Author:** ![CamielPXNX](https://avatars.discourse-cdn.com/v4/letter/c/f0a364/32.png) [@CamielPXNX](https://discuss.huggingface.co/u/CamielPXNX)\
**Post date:** [March 7, 2023, 10:16am UTC](https://discuss.huggingface.co/t/dataset-object-without-classlabel/33029/2 "2023-03-07T10:16:28Z")

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I have the same issue. Maybe I’m looking in the wrong places, but I can’t find much in the guide about casting a feature to `ClassLabel` when making your own dataset… In my case, I have string IOB labels labels to transform to ints.

EDIT: this clued me in to the solution: [How to create custom ClassLabels?](https://discuss.huggingface.co/t/how-to-create-custom-classlabels/13650)

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<div class="post-metadata">

**Author:** ![uygarkurt](https://avatars.discourse-cdn.com/v4/letter/u/f04885/32.png) [@uygarkurt](https://discuss.huggingface.co/u/uygarkurt)\
**Post date:** [March 8, 2023, 9:52am UTC](https://discuss.huggingface.co/t/dataset-object-without-classlabel/33029/3 "2023-03-08T09:52:40Z")

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I solved it. What I did is basically:

> dataset.features[“your\_label”] = Sequence(feature=ClassLabel(names=names\_you\_want))

However I’m not sure if it did have any effect on training.

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<div class="post-metadata">

**Author:** ![lhoestq](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/lhoestq/32/52888_2.png) [@lhoestq](https://discuss.huggingface.co/u/lhoestq)\
**Post date:** [March 8, 2023, 12:21pm UTC](https://discuss.huggingface.co/t/dataset-object-without-classlabel/33029/4 "2023-03-08T12:21:17Z")

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To cast a feature to a sequence of ClassLabel you can use `.cast_column()`:

```python
ds = ds.cast_column("labels", Sequence(ClassLabel(names=label_names)))

```