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--- |
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license: eupl-1.1 |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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task_categories: |
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- feature-extraction |
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--- |
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European legislation from CELLAR/EUROVOC, English entries only of https://huggingface.co/datasets/EuropeanParliament/Eurovoc. |
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This data is enriched with embeddings, ready for semantic search. |
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Last update 16.05.2024: 352011 entries. |
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## Usage |
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### With Pandas / Polars |
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Simply download the parquet file and read with pandas or polars. |
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```python |
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import pandas as pd # or import polars as pd |
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df = pd.read_parquet("CELLAR_EN_16_05_2024.parquet") |
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df |
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``` |
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### With HF datsets |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("do-me/Eurovoc_en") |
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df = ds["train"].to_pandas() |
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df |
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``` |
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## Semantic Search |
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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. |
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After loading the dataset, use the column `embeddings` for semantic search in this way. See the Jupyter notebook for the full processing script. |
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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. |
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```python |
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from model2vec import StaticModel |
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import numpy as np |
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from sklearn.metrics.pairwise import cosine_similarity |
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model = StaticModel.from_pretrained("minishlab/M2V_base_output") |
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query = "social democracy" |
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quer_emb = model.encode(query) |
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embeddings_matrix = np.stack(df['embeddings'].to_numpy()) |
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df["cos_sim"] = cosine_similarity(embeddings_matrix, quer_emb.reshape(1, -1))[:, 0] |
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df = df.sort_values("cos_sim", ascending=False) |
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df |
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``` |
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