SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Fix doc links (#22274)

Browse Source
This commit is contained in:
amyeroberts
2023-03-20 17:07:31 +00:00
committed by GitHub
parent da005253b8
commit 8ac29fe090
8 changed files with 21 additions and 21 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
docs/source/en/tasks/token_classification.mdx
View File

@@ -16,7 +16,7 @@ specific language governing permissions and limitations under the License.
<Youtube id="wVHdVlPScxA"/>
Token classification assigns a label to individual tokens in a sentence. One of the most common token classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence, such as a person, location, or organization.
Token classification assigns a label to individual tokens in a sentence. One of the most common token classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence, such as a person, location, or organization.
This guide will show you how to:
@@ -369,7 +369,7 @@ 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 seqeval scores 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 seqeval scores 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`]: