added dataset loader

open_australian_legal_embeddings_openai.py

@@ -0,0 +1,99 @@
+# Copyright 2024 Rob Kopel.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""An extension of Umar Butler's open-australian-legal-corpus dataset to include 1024 long embeddings from OpenAI's text-embedding-3-large model"""
+
+import json
+import datasets
+
+
+_CITATION = """\
+@misc{open-australian-legal-embeddings-openai,
+    title = {Open Australian Legal Embeddings OpenAI},
+    author={Rob Kopel},
+    year={2024},
+    version={1.0}
+    url={https://huggingface.co/datasets/R0bk/open-australian-legal-embeddings-openai}
+}
+"""
+
+_DESCRIPTION = """\
+An extension of Umar Butler's open-australian-legal-corpus dataset to include 1024 long embeddings from OpenAI's text-embedding-3-large model.
+If you wish to explore or deploy in your environment it can be used with open-australian-legal-explorer on github.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/R0bk/open-australian-legal-embeddings-openai"
+
+_LICENSE = """
+Please see the open-australian-legal-corpus licence [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md).
+"""
+
+_URLS = {
+    'metadatas' : 'data/metadatas.jsonl',
+    'texts' : 'data/texts.jsonl',
+    'embeddings' : 'data/embeddings.jsonl',
+}
+
+class OpenAustralianLegalEmbeddingsOpenai(datasets.GeneratorBasedBuilder):
+    """An extension of Umar Butler's open-australian-legal-corpus dataset to include 1024 long embeddings from OpenAI's text-embedding-3-large model"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    DEFAULT_CONFIG_NAME = "train"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    'version_id' : datasets.Value('string'),
+                    'type' : datasets.Value('string'),
+                    'jurisdiction' : datasets.Value('string'),
+                    'source' : datasets.Value('string'),
+                    'citation' : datasets.Value('string'),
+                    'url' : datasets.Value('string'),
+                    'is_last_chunk' : datasets.Value('bool'),
+                    'chunk_index' : datasets.Value('int'),
+                    'text' : datasets.Value('string'),
+                    'embedding' : [datasets.Value('float32')]
+                }
+            ), 
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    'metadatas_path' : dl_files['metadatas'],
+                    'texts_path' : dl_files['texts'],
+                    'embeddings_path' : dl_files['embeddings'],
+                }
+            )
+        ]
+
+    def _generate_examples(self, embed_path, metas_path, texts_path):
+        with open(embed_path, 'r') as embeds, \
+            open(metas_path, 'r') as metas, \
+            open(texts_path, 'r') as texts:
+
+            for key, (embed, meta, text) in enumerate(zip(embeds, metas, texts)):                
+                yield key, {
+                    **json.loads(meta),
+                    'text': json.loads(text),
+                    'embedding': json.loads(embed)
+                }