From 8ac29fe0905c3f7f04963ef22c4f61e59ee1004a Mon Sep 17 00:00:00 2001 From: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Date: Mon, 20 Mar 2023 17:07:31 +0000 Subject: [PATCH] Fix doc links (#22274) --- docs/source/en/tasks/image_classification.mdx | 6 +++--- docs/source/en/tasks/multiple_choice.mdx | 2 +- docs/source/en/tasks/semantic_segmentation.mdx | 4 ++-- docs/source/en/tasks/sequence_classification.mdx | 6 +++--- docs/source/en/tasks/summarization.mdx | 10 +++++----- docs/source/en/tasks/token_classification.mdx | 4 ++-- docs/source/en/tasks/translation.mdx | 8 ++++---- docs/source/es/tasks/summarization.mdx | 2 +- 8 files changed, 21 insertions(+), 21 deletions(-) diff --git a/docs/source/en/tasks/image_classification.mdx b/docs/source/en/tasks/image_classification.mdx index c3faa6145e..fbc8ab6787 100644 --- a/docs/source/en/tasks/image_classification.mdx +++ b/docs/source/en/tasks/image_classification.mdx @@ -403,9 +403,9 @@ Configure the model for training with `compile()`: >>> model.compile(optimizer=optimizer, loss=loss) ``` -To compute the accuracy from the predictions and push your model to the πŸ€— Hub, use [Keras callbacks](./main_classes/keras_callbacks). -Pass your `compute_metrics` function to [KerasMetricCallback](./main_classes/keras_callbacks#transformers.KerasMetricCallback), -and use the [PushToHubCallback](./main_classes/keras_callbacks#transformers.PushToHubCallback) to upload the model: +To compute the accuracy from the predictions and push your model to the πŸ€— Hub, use [Keras callbacks](../main_classes/keras_callbacks). +Pass your `compute_metrics` function to [KerasMetricCallback](../main_classes/keras_callbacks#transformers.KerasMetricCallback), +and use the [PushToHubCallback](../main_classes/keras_callbacks#transformers.PushToHubCallback) to upload the model: ```py >>> from transformers.keras_callbacks import KerasMetricCallback, PushToHubCallback diff --git a/docs/source/en/tasks/multiple_choice.mdx b/docs/source/en/tasks/multiple_choice.mdx index cafe9d5769..874d229859 100644 --- a/docs/source/en/tasks/multiple_choice.mdx +++ b/docs/source/en/tasks/multiple_choice.mdx @@ -341,7 +341,7 @@ 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`]: diff --git a/docs/source/en/tasks/semantic_segmentation.mdx b/docs/source/en/tasks/semantic_segmentation.mdx index a768376178..ffaa251688 100644 --- a/docs/source/en/tasks/semantic_segmentation.mdx +++ b/docs/source/en/tasks/semantic_segmentation.mdx @@ -412,7 +412,7 @@ Convert your datasets to the `tf.data.Dataset` format using the [`~datasets.Data ... ) ``` -To compute the accuracy from the predictions and push your model to the πŸ€— Hub, use [Keras callbacks](./main_classes/keras_callbacks). +To compute the accuracy from the predictions and push your model to the πŸ€— Hub, use [Keras callbacks](../main_classes/keras_callbacks). Pass your `compute_metrics` function to [`KerasMetricCallback`], and use the [`PushToHubCallback`] to upload the model: @@ -587,4 +587,4 @@ To visualize the results, load the [dataset color palette](https://github.com/te
Image of bedroom overlaid with segmentation map -
\ No newline at end of file + diff --git a/docs/source/en/tasks/sequence_classification.mdx b/docs/source/en/tasks/sequence_classification.mdx index 20a3f4d146..b4c57b3fa6 100644 --- a/docs/source/en/tasks/sequence_classification.mdx +++ b/docs/source/en/tasks/sequence_classification.mdx @@ -16,7 +16,7 @@ specific language governing permissions and limitations under the License. -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 πŸ™‚ positive, πŸ™ 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 πŸ™‚ positive, πŸ™ 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`]: diff --git a/docs/source/en/tasks/summarization.mdx b/docs/source/en/tasks/summarization.mdx index 71b1f94d8a..efb2db60bd 100644 --- a/docs/source/en/tasks/summarization.mdx +++ b/docs/source/en/tasks/summarization.mdx @@ -19,7 +19,7 @@ specific language governing permissions and limitations under the License. Summarization creates a shorter version of a document or an article that captures all the important information. Along with translation, it is another example of a task that can be formulated as a sequence-to-sequence task. Summarization can be: - Extractive: extract the most relevant information from a document. -- Abstractive: generate new text that captures the most relevant information. +- Abstractive: generate new text that captures the most relevant information. This guide will show you how to: @@ -275,7 +275,7 @@ 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 ROUGE score 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 ROUGE score 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`]: @@ -354,7 +354,7 @@ Tokenize the text and return the `input_ids` as PyTorch tensors: >>> inputs = tokenizer(text, return_tensors="pt").input_ids ``` -Use the [`~transformers.generation_utils.GenerationMixin.generate`] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](./main_classes/text_generation) API. +Use the [`~transformers.generation_utils.GenerationMixin.generate`] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API. ```py >>> from transformers import AutoModelForSeq2SeqLM @@ -380,7 +380,7 @@ Tokenize the text and return the `input_ids` as TensorFlow tensors: >>> inputs = tokenizer(text, return_tensors="tf").input_ids ``` -Use the [`~transformers.generation_tf_utils.TFGenerationMixin.generate`] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](./main_classes/text_generation) API. +Use the [`~transformers.generation_tf_utils.TFGenerationMixin.generate`] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API. ```py >>> from transformers import TFAutoModelForSeq2SeqLM @@ -396,4 +396,4 @@ Decode the generated token ids back into text: 'the inflation reduction act lowers prescription drug costs, health care costs, and energy costs. it's the most aggressive action on tackling the climate crisis in american history. it will ask the ultra-wealthy and corporations to pay their fair share.' ``` - \ No newline at end of file + diff --git a/docs/source/en/tasks/token_classification.mdx b/docs/source/en/tasks/token_classification.mdx index a78922fd8c..7f6032a0ec 100644 --- a/docs/source/en/tasks/token_classification.mdx +++ b/docs/source/en/tasks/token_classification.mdx @@ -16,7 +16,7 @@ specific language governing permissions and limitations under the License. -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`]: diff --git a/docs/source/en/tasks/translation.mdx b/docs/source/en/tasks/translation.mdx index 6f3d1260d1..df9df832da 100644 --- a/docs/source/en/tasks/translation.mdx +++ b/docs/source/en/tasks/translation.mdx @@ -284,7 +284,7 @@ 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 SacreBLEU metric 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 SacreBLEU metric 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`]: @@ -362,7 +362,7 @@ Tokenize the text and return the `input_ids` as PyTorch tensors: >>> inputs = tokenizer(text, return_tensors="pt").input_ids ``` -Use the [`~transformers.generation_utils.GenerationMixin.generate`] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](./main_classes/text_generation) API. +Use the [`~transformers.generation_utils.GenerationMixin.generate`] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API. ```py >>> from transformers import AutoModelForSeq2SeqLM @@ -388,7 +388,7 @@ Tokenize the text and return the `input_ids` as TensorFlow tensors: >>> inputs = tokenizer(text, return_tensors="tf").input_ids ``` -Use the [`~transformers.generation_tf_utils.TFGenerationMixin.generate`] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](./main_classes/text_generation) API. +Use the [`~transformers.generation_tf_utils.TFGenerationMixin.generate`] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API. ```py >>> from transformers import TFAutoModelForSeq2SeqLM @@ -404,4 +404,4 @@ Decode the generated token ids back into text: 'Les lugumes partagent les ressources avec des bactΓ©ries fixatrices d'azote.' ``` - \ No newline at end of file + diff --git a/docs/source/es/tasks/summarization.mdx b/docs/source/es/tasks/summarization.mdx index c09c4b0b83..066ac288b4 100644 --- a/docs/source/es/tasks/summarization.mdx +++ b/docs/source/es/tasks/summarization.mdx @@ -219,4 +219,4 @@ Para un ejemplo con mayor profundidad de cΓ³mo hacer fine-tuning a un modelo par [notebook en PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization.ipynb) o a la [notebook en TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb). - \ No newline at end of file +