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Fix doc links (#22274)

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amyeroberts
2023-03-20 17:07:31 +00:00
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docs/source/en/tasks/sequence_classification.mdx
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@@ -16,7 +16,7 @@ specific language governing permissions and limitations under the License.
<Youtube id="leNG9fN9FQU"/>
Text classification is a common NLP task that assigns a label or class to text. Some of the largest companies run text classification in production for a wide range of practical applications. One of the most popular forms of text classification is sentiment analysis, which assigns a label like 🙂 positive, 🙁 negative, or 😐 neutral to a sequence of text.
Text classification is a common NLP task that assigns a label or class to text. Some of the largest companies run text classification in production for a wide range of practical applications. One of the most popular forms of text classification is sentiment analysis, which assigns a label like 🙂 positive, 🙁 negative, or 😐 neutral to a sequence of text.
This guide will show you how to:
@@ -69,7 +69,7 @@ Then take a look at an example:
}
```
There are two fields in this dataset:
There are two fields in this dataset:
- `text`: the movie review text.
- `label`: a value that is either `0` for a negative review or `1` for a positive review.
@@ -267,7 +267,7 @@ Configure the model for training with [`compile`](https://keras.io/api/models/mo
>>> model.compile(optimizer=optimizer)
```
The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](./main_classes/keras_callbacks).
The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).
Pass your `compute_metrics` function to [`~transformers.KerasMetricCallback`]: