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README.md
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---
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language:
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- cs
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license:
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- cc-by-sa-3.0
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- gfdl
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size_categories:
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- 100K<n<1M
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task_categories:
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- text-generation
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- fill-mask
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dataset_info:
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features:
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splits:
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download_size:
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dataset_size: 2580729273
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configs:
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- config_name: default
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---
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This dataset contains the Czech subset of the [`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia) dataset. Each page is divided into paragraphs, stored as a list in the `chunks` column. For every paragraph, embeddings are created using the [`Seznam/simcse-dist-mpnet-paracrawl-cs-en`](https://huggingface.co/Seznam/simcse-dist-mpnet-paracrawl-cs-en) model.
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import textwrap
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import sentence_transformers
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ds.set_format(type="torch", columns=["embeddings"], output_all_columns=True)
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chunk = ds_flat[hit['corpus_id']]['chunk']
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print(f"[{hit['score']:0.2f}] {textwrap.shorten(chunk, width=100, placeholder='…')} [{title}]")
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# [0.
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# [0.
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# ...
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```
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</details>
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The embeddings generation took about
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## License
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---
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dataset_info:
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features:
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- name: id
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dtype: string
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- name: url
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dtype: string
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- name: title
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dtype: string
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- name: chunks
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sequence: string
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- name: embeddings
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sequence:
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sequence: float32
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splits:
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- name: train
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num_bytes: 2580729273
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num_examples: 534044
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download_size: 2307703671
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dataset_size: 2580729273
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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language:
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- cs
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size_categories:
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- 100K<n<1M
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task_categories:
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- text-generation
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- fill-mask
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license:
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- cc-by-sa-3.0
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- gfdl
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---
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This dataset contains the Czech subset of the [`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia) dataset. Each page is divided into paragraphs, stored as a list in the `chunks` column. For every paragraph, embeddings are created using the [`Seznam/simcse-dist-mpnet-paracrawl-cs-en`](https://huggingface.co/Seznam/simcse-dist-mpnet-paracrawl-cs-en) model.
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import textwrap
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import sentence_transformers
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from sentence_transformers.models import Transformer, Pooling
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from sentence_transformers import SentenceTransformer
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from sentence_transformers.models import Transformer, Pooling
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embedding_model = Transformer("Seznam/simcse-dist-mpnet-paracrawl-cs-en")
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pooling = Pooling(word_embedding_dimension=embedding_model.get_word_embedding_dimension(), pooling_mode="cls")
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model = SentenceTransformer(modules=[embedding_model, pooling])
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ds.set_format(type="torch", columns=["embeddings"], output_all_columns=True)
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chunk = ds_flat[hit['corpus_id']]['chunk']
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print(f"[{hit['score']:0.2f}] {textwrap.shorten(chunk, width=100, placeholder='…')} [{title}]")
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# [0.72] Molekulová fyzika ( též molekulární fyzika ) je část fyziky, která zkoumá látky na úrovni atomů a… [Molekulová fyzika]
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# [0.70] Fyzika ( z řeckého φυσικός ( fysikos ): přírodní, ze základu φύσις ( fysis ): příroda, archaicky… [Fyzika]
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# ...
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```
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</details>
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The embeddings generation took about 35 minutes on an NVIDIA A100 80GB.
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## License
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