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docs: Resolve typos, missing import #3443

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Jul 26, 2023
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docs: Resolve typos, missing import #3443

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7 changes: 4 additions & 3 deletions docs/_source/guides/train_a_model.md
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Expand Up @@ -89,6 +89,7 @@ Options:

```python
import argilla as rg
from argilla.training import ArgillaTrainer
from datasets import load_dataset

dataset_rg = rg.DatasetForTokenClassification.from_datasets(
Expand Down Expand Up @@ -126,18 +127,18 @@ It is possible to directly include train-test splits to the `prepare_for_trainin
*TextClassification*

For text classification tasks, it flattens the inputs into separate columns of the returned dataset and converts the annotations of your records into integers and writes them in a label column:
By passing the `framework` variable as `setfit`, `transformers`, `spark-nlp` or `spacy`. This task requires a `DatastForTextClassification`.
By passing the `framework` variable as `setfit`, `transformers`, `spark-nlp` or `spacy`. This task requires a `DatasetForTextClassification`.


*TokenClassification*

For token classification tasks, it converts the annotations of a record into integers representing BIO tags and writes them in a `ner_tags` column:
By passing the `framework` variable as `transformers`, `spark-nlp` or `spacy`. This task requires a `DatastForTokenClassification`.
By passing the `framework` variable as `transformers`, `spark-nlp` or `spacy`. This task requires a `DatasetForTokenClassification`.

*Text2Text*

For text generation tasks like `summarization` and translation tasks, it converts the annotations of a record `text` and `target` columns.
By passing the `framework` variable as `transformers` and `spark-nlp`. This task requires a `DatastForText2Text`.
By passing the `framework` variable as `transformers` and `spark-nlp`. This task requires a `DatasetForText2Text`.

*Feedback*
For feedback-oriented datasets, we currently rely on a fully customizable workflow, which means automation is limited and yet to be thought out.
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