Add docs

docs/source/index.rst

@@ -21,8 +21,10 @@ Lightning Flash
 
    reference/task
    reference/image_classification
+   reference/summarization
    reference/text_classification
    reference/tabular_classification
+   reference/translation
 
 .. toctree::
    :maxdepth: 1

docs/source/reference/summarization.rst

@@ -0,0 +1,178 @@
+.. _summarization:
+
+#############
+Summarization
+#############
+
+********
+The task
+********
+
+Summarization is the task of summarizing text from a larger document/article into a short sentence/description. For example, taking a web article and describing the topic in a short sentence.
+This task is a subset of Sequence to Sequence tasks, which requires the model to generate a variable length sequence given an input sequence. In our case the article would be our input sequence, and the short description/sentence would be the output sequence from the model.
+
+-----
+
+*********
+Inference
+*********
+
+The :class:`~flash.text.SummarizationTask` is already pre-trained on [XSUM](https://arxiv.org/abs/1808.08745), a dataset of online British Broadcasting Corporation articles.
+
+Use the :class:`~flash.text.SummarizationTask` pretrained model for inference on any string sequence using :func:`~flash.text.SummarizationTask.predict`:
+
+.. code-block:: python
+
+	# import our libraries
+	from flash.text import SummarizationTask
+
+
+    # 2. Load the model from a checkpoint
+    model = SummarizationTask.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/summarization_model_xsum.pt")
+
+    # 2. Perform inference from a sequence
+    predictions = model.predict([
+        """
+        Camilla bought a box of mangoes with a Brixton £10 note, introduced last year to try to keep the money of local
+        people within the community.The couple were surrounded by shoppers as they walked along Electric Avenue.
+        They came to Brixton to see work which has started to revitalise the borough.
+        It was Charles' first visit to the area since 1996, when he was accompanied by the former
+        South African president Nelson Mandela.Greengrocer Derek Chong, who has run a stall on Electric Avenue
+        for 20 years, said Camilla had been ""nice and pleasant"" when she purchased the fruit.
+        ""She asked me what was nice, what would I recommend, and I said we've got some nice mangoes.
+        She asked me were they ripe and I said yes - they're from the Dominican Republic.""
+        Mr Chong is one of 170 local retailers who accept the Brixton Pound.
+        Customers exchange traditional pound coins for Brixton Pounds and then spend them at the market
+        or in participating shops.
+        During the visit, Prince Charles spent time talking to youth worker Marcus West, who works with children
+        nearby on an estate off Coldharbour Lane. Mr West said:
+        ""He's on the level, really down-to-earth. They were very cheery. The prince is a lovely man.""
+        He added: ""I told him I was working with young kids and he said, 'Keep up all the good work.'""
+        Prince Charles also visited the Railway Hotel, at the invitation of his charity The Prince's Regeneration Trust.
+        The trust hopes to restore and refurbish the building,
+        where once Jimi Hendrix and The Clash played, as a new community and business centre."
+        """
+    ])
+    print(predictions)
+
+Or on a given dataset:
+
+.. code-block:: python
+
+	# import our libraries
+	from flash import download_data
+	from flash.text import SummarizationTask
+
+    # 2. Load the model from a checkpoint
+    model = SummarizationTask.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/summarization_model_xsum.pt")
+
+	# 3. Perform inference from a csv file
+	predictions = model.predict("data/xsum/predict.csv")
+	print(predictions)
+
+For more advanced inference options, see :ref:`predictions`.
+
+-----
+
+**********
+Finetuning
+**********
+
+Say you want to finetune to your own summarization data. We use the XSUM dataset as an example which contains a ``train.csv`` and ``valid.csv``, structured like so:
+
+.. code-block::
+
+    input,target
+    "The researchers have sequenced the genome of a strain of bacterium that causes the virulent infection...","A team of UK scientists hopes to shed light on the mysteries of bleeding canker, a disease that is threatening the nation's horse chestnut trees."
+    "Knight was shot in the leg by an unknown gunman at Miami's Shore Club where West was holding a pre-MTV Awards...",Hip hop star Kanye West is being sued by Death Row Records founder Suge Knight over a shooting at a beach party in August 2005.
+    ...
+
+In the above the input column represents the long articles/documents, and the target is the short description used as the target.
+
+All we need is three lines of code to train our model!
+
+.. code-block:: python
+
+	# import our libraries
+	import flash
+	from flash import download_data
+	from flash.text import SummarizationData, SummarizationTask
+
+    # 1. Download data
+    download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", 'data/')
+
+    # Organize the data
+    datamodule = SummarizationData.from_files(
+        train_file="data/xsum/train.csv",
+        valid_file="data/xsum/valid.csv",
+        test_file="data/xsum/test.csv",
+        input="input",
+        target="target"
+    )
+
+    # 2. Build the task
+    model = SummarizationTask()
+
+    # 4. Create trainer
+    trainer = flash.Trainer(max_epochs=1, gpus=1)
+
+    # 5. Finetune the task
+    trainer.finetune(model, datamodule=datamodule)
+
+    # 6. Save trainer task
+    trainer.save_checkpoint("summarization_model_xsum.pt")
+
+----
+
+To run the example:
+
+.. code-block:: bash
+
+    python flash_examples/finetuning/summarization.py
+
+
+------
+
+*********************
+Changing the backbone
+*********************
+By default, we use the `t5 <https://arxiv.org/abs/1910.10683>`_ model for summarization. You can change the model run by passing in the backbone parameter.
+
+.. note:: When changing the backbone, make sure you pass in the same backbone to the Task and the Data object! Since this is a Seq2Seq task, make sure you use a Seq2Seq model.
+
+.. code-block:: python
+
+    datamodule = SummarizationData.from_files(
+        train_file="data/wmt_en_ro/train.csv",
+        valid_file="data/wmt_en_ro/valid.csv",
+        test_file="data/wmt_en_ro/test.csv",
+        input="input",
+        target="target",
+        backbone="google/mt5-small",
+    )
+
+    model = SummarizationTask(backbone="google/mt5-small")
+
+------
+
+*************
+API reference
+*************
+
+.. _summarization_task:
+
+SummarizationTask
+--------------
+
+.. autoclass:: flash.text.summarization.model.SummarizationTask
+    :members:
+    :exclude-members: forward
+
+.. _summarization_data:
+
+SummarizationData
+----------------------
+
+.. autoclass:: flash.text.summarization.data.SummarizationData
+
+.. automethod:: flash.text.summarization.data.SummarizationData.from_files

docs/source/reference/text_classification.rst

@@ -16,9 +16,9 @@ Text classification is the task of assigning a piece of text (word, sentence or
 Inference
 *********
 
-The :class:`~flash.text.TextClassificatier` is already pre-trained on [IMDB](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews), a dataset of highly polarized movie reviews, trained for binary classification- to predict if a given review has a positive or negative sentiment.
+The :class:`~flash.text.TextClassifier` is already pre-trained on [IMDB](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews), a dataset of highly polarized movie reviews, trained for binary classification- to predict if a given review has a positive or negative sentiment.
 
-Use the :class:`~flash.text.TextClassificatier` pretrained model for inference on any string sequence using :func:`~flash.text.TextClassifier.predict`:
+Use the :class:`~flash.text.TextClassifier` pretrained model for inference on any string sequence using :func:`~flash.text.TextClassifier.predict`:
 
 .. code-block:: python
 
@@ -166,6 +166,3 @@ TextClassificationData
 .. autoclass:: flash.text.classification.data.TextClassificationData
 
 .. automethod:: flash.text.classification.data.TextClassificationData.from_files
-
-
-

docs/source/reference/translation.rst

@@ -0,0 +1,160 @@
+.. _translation:
+
+###################
+Translation
+###################
+
+********
+The task
+********
+
+Translation is the task of translating text from a source language to another, such as English to Romanian.
+This task is a subset of Sequence to Sequence tasks, which requires the model to generate a variable length sequence given an input sequence. In our case the English text would be our input sequence, and the Romanian sentence would be the output sequence from the model.
+
+-----
+
+*********
+Inference
+*********
+
+The :class:`~flash.text.TranslationTask` is already pre-trained on [WMT16 English/Romanian](https://www.statmt.org/wmt16/translation-task.html), a dataset of English to Romanian samples, based on the Europarl corpora.
+
+Use the :class:`~flash.text.TranslationTask` pretrained model for inference on any string sequence using :func:`~flash.text.TranslationTask.predict`:
+
+.. code-block:: python
+
+	# import our libraries
+	from flash.text import TranslationTask
+
+
+    # 2. Load the model from a checkpoint
+    model = TranslationTask.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/translation_model_en_ro.pt")
+
+    # 2. Perform inference from list of sequences
+    predictions = model.predict([
+        "BBC News went to meet one of the project's first graduates.",
+        "A recession has come as quickly as 11 months after the first rate hike and as long as 86 months.",
+    ])
+    print(predictions)
+
+Or on a given dataset:
+
+.. code-block:: python
+
+	# import our libraries
+	from flash import download_data
+	from flash.text import TranslationTask
+
+    # 2. Load the model from a checkpoint
+    model = TranslationTask.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/translation_model_en_ro.pt")
+
+	# 3. Perform inference from a csv file
+	predictions = model.predict("data/wmt_en_ro/predict.csv")
+	print(predictions)
+
+For more advanced inference options, see :ref:`predictions`.
+
+-----
+
+**********
+Finetuning
+**********
+
+Say you want to finetune to your own translation data. We use the English/Romanian WMT16 dataset as an example which contains a ``train.csv`` and ``valid.csv``, structured like so:
+
+.. code-block::
+
+    input,target
+    "Written statements and oral questions (tabling): see Minutes","Declaraţii scrise şi întrebări orale (depunere): consultaţi procesul-verbal"
+    "Closure of sitting","Ridicarea şedinţei"
+    ...
+
+In the above the input/target columns represent the English and Romanian translation respectively.
+
+All we need is three lines of code to train our model!
+
+.. code-block:: python
+
+	# import our libraries
+	import flash
+	from flash import download_data
+	from flash.text import TranslationData, TranslationTask
+
+    # 1. Download data
+    download_data("https://pl-flash-data.s3.amazonaws.com/wmt_en_ro.zip", 'data/')
+
+    # Organize the data
+    datamodule = TranslationData.from_files(
+        train_file="data/wmt_en_ro/train.csv",
+        valid_file="data/wmt_en_ro/valid.csv",
+        test_file="data/wmt_en_ro/test.csv",
+        input="input",
+        target="target",
+    )
+
+    # 2. Build the task
+    model = TranslationTask()
+
+    # 4. Create trainer
+    trainer = flash.Trainer(max_epochs=5, gpus=1, precision=16)
+
+    # 5. Finetune the task
+    trainer.finetune(model, datamodule=datamodule)
+
+    # 6. Save trainer task
+    trainer.save_checkpoint("translation_model_en_ro.pt")
+
+----
+
+To run the example:
+
+.. code-block:: bash
+
+    python flash_examples/finetuning/translation.py
+
+
+------
+
+*********************
+Changing the backbone
+*********************
+By default, we use the `MarianNMT <https://marian-nmt.github.io/>`_ model for translation. You can change the model run by passing in the backbone parameter.
+
+.. note:: When changing the backbone, make sure you pass in the same backbone to the Task and the Data object! Since this is a Seq2Seq task, make sure you use a Seq2Seq model.
+
+.. code-block:: python
+
+    datamodule = TranslationData.from_files(
+        train_file="data/wmt_en_ro/train.csv",
+        valid_file="data/wmt_en_ro/valid.csv",
+        test_file="data/wmt_en_ro/test.csv",
+        input="input",
+        target="target",
+        backbone="t5-small",
+    )
+
+    model = TranslationTask(backbone="t5-small")
+
+------
+
+*************
+API reference
+*************
+
+.. _translation_task:
+
+TranslationTask
+--------------
+
+.. autoclass:: flash.text.translation.model.TranslationTask
+    :members:
+    :exclude-members: forward
+
+.. _translation_data:
+
+TranslationData
+----------------------
+
+.. autoclass:: flash.text.translation.data.TranslationData
+
+.. automethod:: flash.text.translation.data.TranslationData.from_files