From bbb14aca728f6804c656ee81549bc59ce8975c60 Mon Sep 17 00:00:00 2001 From: Brian Lee Date: Tue, 30 Jan 2024 13:50:03 -0500 Subject: [PATCH] Update docs - address PR comments from #1145 (#1147) --- docs/getting_started/quickstart.md | 24 +++++++++------ docs/getting_started/quickstart_python.md | 14 +++++---- docs/poetry.lock | 37 +++++++++++++++++++---- lilac/data/dataset.py | 4 +-- 4 files changed, 55 insertions(+), 24 deletions(-) diff --git a/docs/getting_started/quickstart.md b/docs/getting_started/quickstart.md index 3c9e621f..cb0f6bd1 100644 --- a/docs/getting_started/quickstart.md +++ b/docs/getting_started/quickstart.md @@ -41,7 +41,8 @@ Fill in HuggingFace-specific fields: ```{note} Lilac's sweet spot is ~100K-1M rows of data, although up to 10 million rows are possible. This quickstart uses 10,000 rows so that clustering and embedding operations finish locally -in ~10 minutes even without a GPU. +in 10-20 minutes even without a GPU. [Lilac Garden](https://docs.lilacml.com/blog/introducing-garden.html) can help speed +up your computation for larger datasets. ``` Finally: @@ -61,14 +62,16 @@ When we load a dataset, Lilac creates a default UI configuration, inferring whic presented differently in the UI. Let's edit the configuration by clicking the `Dataset settings` button in the top-right corner. If -your media field contains markdown, you can enable markdown rendering. +your media field contains markdown, you can +[enable markdown rendering](../datasets/dataset_configure.md). ## Cluster -Lilac can detect clusters in your dataset. Clusters are a powerful way to understand the types of -content present in your dataset, as well as to target subsets for removal from the dataset. +Lilac can detect [clusters in your dataset](../datasets/dataset_cluster.md). Clusters are a powerful +way to understand the types of content present in your dataset, as well as to target subsets for +removal from the dataset. To cluster, open up the dataset schema tray to reveal the fields in your dataset. Here, you can choose which field will get clustered. @@ -80,9 +83,10 @@ other fields in your dataset by changing the Explore and Group By selections. ## Tagging and Deleting rows -Lilac can curate your dataset by tagging or deleting rows. +Lilac can curate your dataset by [tagging](../datasets/dataset_labels.md) or +[deleting](../datasets/dataset_delete_rows.md) rows. -Deleting is not permanent - you can toggle visibility of deleted items - but it is a convenient way +Deleting is not permanent - you can toggle visibility of deleted item. Deleting is a convenient way to iterate on your dataset by removing undesired slices of data. Later on, when you export data from Lilac, deleted rows will be excluded by default. @@ -124,10 +128,10 @@ can open the statistics panel to see the distribution of concept scores. ## Download -Now that we've clustered, curated, and enriched the dataset, let's download it by clicking on the -`Download data` button in the top-right corner. This will download a json file with the same name as -the dataset. Once we have the data, we can continue working with it in a Python notebook, or any -other language. +Now that we've clustered, curated, and enriched the dataset, let's +[download it](../datasets/dataset_export.md) by clicking on the `Download data` button in the +top-right corner. This will download a json file with the same name as the dataset. Once we have the +data, we can continue working with it in a Python notebook, or any other language. You can also get the dataset as a Pandas dataframe through the [Python API](quickstart_python.md). diff --git a/docs/getting_started/quickstart_python.md b/docs/getting_started/quickstart_python.md index 3c6ac9e9..14b5a954 100644 --- a/docs/getting_started/quickstart_python.md +++ b/docs/getting_started/quickstart_python.md @@ -66,7 +66,7 @@ dataset = ll.get_dataset('local', 'open-orca-100k') ## Compute clusters -Let's compute clusters on the `question`field. +Let's [compute clusters](../datasets/dataset_cluster.md#from-python) on the `question`field. ```python dataset.cluster('question') @@ -130,8 +130,8 @@ question__cluster: ## Select specific rows Let's find all clusters that talk about movies via [](#Dataset.select_rows), which works very -similarly to a `SQL Select` statement. We do this by adding an [`exists`](#Filter.op) filter on -`question__cluster.cluster_title`. +similarly to a `SQL Select` statement. We do this by adding an [`regex_matches`](#Filter.op) filter +on `question__cluster.cluster_title`. (See [Querying](../datasets/dataset_query.md) for more.) ```py df_with_emails = dataset.select_rows( @@ -177,8 +177,9 @@ For more information on querying, see [](#Dataset.select_rows). ### Profanity detection Let's also run the profanity concept on the `response` field to see if the LLM produced any profane -content. To do that we need to _index_ the `response` field using a text embedding. We only need to -index once. For a fast on-device embedding, we recommend the +content. To do that we need to _index_ the `response` field using a +[text embedding](../datasets/dataset_embeddings.md#from-python). We only need to index once. For a +fast on-device embedding, we recommend the [GTE-Small embedding](https://huggingface.co/thenlper/gte-small). ```py @@ -221,7 +222,8 @@ Computing signal "concept_score" on local/open-orca-10k:('response',) took 0.025 4 [{'__span__': {'start': 0, 'end': 164}, 'score... ``` -To compute the concept score over the entire dataset, we do: +To compute the [concept score](../datasets/dataset_concepts.md#from-python) over the entire dataset, +we do: ```py dataset.compute_concept('lilac', 'profanity', embedding='gte-small', path='response') diff --git a/docs/poetry.lock b/docs/poetry.lock index 67eaaeed..cfb92adf 100644 --- a/docs/poetry.lock +++ b/docs/poetry.lock @@ -2149,6 +2149,7 @@ files = [ {file = "numpy-1.26.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:3c67423b3703f8fbd90f5adaa37f85b5794d3366948efe9a5190a5f3a83fc34e"}, {file = "numpy-1.26.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = 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b/lilac/data/dataset.py @@ -451,8 +451,8 @@ def count( Args: filters: Filters to apply to the row; only matching rows will be counted. - limit: Limit the number of rows to count. (The returned answer may be smaller than LIMIT, - if filters reduce the row count to below the limit!) + limit: Limit the number of rows to count. The returned answer may be smaller than LIMIT, + if filters reduce the row count to below the limit! include_deleted: Whether to include deleted rows in the count. Returns: