Add lancedb ner example #912
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zilto
reviewed
May 21, 2024
zilto
reviewed
May 21, 2024
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ah sure, that works.
…On Tue, May 21, 2024 at 8:19 AM Thierry Jean ***@***.***> wrote:
***@***.**** commented on this pull request.
------------------------------
In hamilton/plugins/huggingface_extensions.py
<#912 (comment)>
:
> + keep_in_memory: Optional[bool] = None
+ save_infos: bool = False
+ revision: Optional[Union[str, Version]] = None
+ token: Optional[Union[bool, str]] = None
+ use_auth_token = "deprecated"
+ task = "deprecated"
+ streaming: bool = False
+ num_proc: Optional[int] = None
+ storage_options: Optional[Dict] = None
+ config_kwargs: Optional[Dict] = None
+
+ @classmethod
+ def applicable_types(cls) -> Collection[Type]:
+ return list(HF_types)
+
+ def _get_loading_kwargs(self) -> dict:
Might want to look at this approach:
https://github.com/DAGWorks-Inc/hamilton/blob/818f2fca8b800b1a2b66fea31fdd73d5534beed8/hamilton/plugins/dlt_extensions.py#L96
I think fields_to_skip is a clear approach. Could document further why to
skip in the docstring
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elijahbenizzy
approved these changes
May 21, 2024
This shows how one might build a pipeline and utilize models to extract entities and embeddings. Then save them to lancedb, and then use both to query over them. WIP (+4 squashed commits) Squashed commits: [c91afb6] wip [17cd297] Gets example to run on HF datasets properly TODOs: - tidy up - README - remove parallel in favor of discussion [f840934] TODOs: 1. remove parallel - doesn't make sense for GPU case as you can't parallelize that, and you want to use datasets.map() for batching. 2. make it run on datasets [b76d011] WIP create lanceDB NER example
This adds support for loading hugging face datasets. It then also supports saving it to parquet and to lancedb. Adds tests. Putting lancedb saver here is arbitrary, but because we would need to check installed dependencies either way, I felt it would be simpler to put here for now. Ideally we could convert between common formats to help here. E.g. pyarrow tables could be something to simplify things.
Say we have this - and want to save it with a saver: ```python def foo() -> Union[int, float]: return ... ``` If the saver's applicable_types is [int, float], this would previously fail, now it does not. Added test for this. If the saver's applicable_type was just `float` or `int`, then rightly this fails -- added test for that explicitly.
Also cleans up notebook and adds comments to code
Makes some changes to make sure things run on google collab. Plus some minor documentation / wording updates.
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See commits.
This is almost there expect for the example code -- need to also create a notebook for this to run properly.
Changes
How I tested this
Notes
Checklist