license: eupl-1.1
language:
- en
size_categories:
- 100K<n<1M
task_categories:
- feature-extraction
European legislation from CELLAR/EUROVOC, English entries only of https://huggingface.co/datasets/EuropeanParliament/Eurovoc. This data is enriched with embeddings, ready for semantic search.
Last update 16.05.2024: 352011 entries.
Usage
With Pandas / Polars
Simply download the parquet file and read with pandas or polars.
import pandas as pd # or import polars as pd
df = pd.read_parquet("CELLAR_EN_16_05_2024.parquet")
df
With HF datsets
from datasets import load_dataset
ds = load_dataset("do-me/Eurovoc_en")
df = ds["train"].to_pandas()
df
Semantic Search
Testwise, the first 512 tokens of every text have been inferenced with the model2vec library and https://huggingface.co/minishlab/M2V_base_output model from @minishlab.
After loading the dataset, use the column embeddings for semantic search in this way. See the Jupyter notebook for the full processing script.
You can re-run it on consumer-grade hardware without GPU. Inferencing took Wall time: 1min 36s on an M3 Max. Inferecing the entire text takes 50 mins but yields poor quality, currently investigating.
from model2vec import StaticModel
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
model = StaticModel.from_pretrained("minishlab/M2V_base_output")
query = "social democracy"
quer_emb = model.encode(query)
embeddings_matrix = np.stack(df['embeddings'].to_numpy())
df["cos_sim"] = cosine_similarity(embeddings_matrix, quer_emb.reshape(1, -1))[:, 0]
df = df.sort_values("cos_sim", ascending=False)
df
