Update the README

README.md

@@ -1,38 +1,43 @@
 ---
-language:
-- cs
-license:
-- cc-by-sa-3.0
-- gfdl
-size_categories:
-- 100K<n<1M
-task_categories:
-- text-generation
-- fill-mask
 dataset_info:
   features:
-  - name: id
-    dtype: string
-  - name: url
-    dtype: string
-  - name: title
-    dtype: string
-  - name: chunks
-    sequence: string
-  - name: embeddings
-    sequence:
-      sequence: float32
+    - name: id
+      dtype: string
+    - name: url
+      dtype: string
+    - name: title
+      dtype: string
+    - name: chunks
+      sequence: string
+    - name: embeddings
+      sequence:
+        sequence: float32
   splits:
-  - name: train
-    num_bytes: 2580729273
-    num_examples: 534044
-  download_size: 2307157994
+    - name: train
+      num_bytes: 2580729273
+      num_examples: 534044
+  download_size: 2307703671
   dataset_size: 2580729273
+
 configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+  - config_name: default
+    data_files:
+    - split: train
+      path: data/train-*
+
+language:
+  - cs
+
+size_categories:
+  - 100K<n<1M
+
+task_categories:
+  - text-generation
+  - fill-mask
+
+license:
+  - cc-by-sa-3.0
+  - gfdl
 ---
 
 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.
@@ -107,8 +112,14 @@ import os
 import textwrap
 
 import sentence_transformers
+from sentence_transformers.models import Transformer, Pooling
+
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.models import Transformer, Pooling
 
-model = sentence_transformers.SentenceTransformer("Seznam/simcse-dist-mpnet-paracrawl-cs-en")
+embedding_model = Transformer("Seznam/simcse-dist-mpnet-paracrawl-cs-en")
+pooling = Pooling(word_embedding_dimension=embedding_model.get_word_embedding_dimension(), pooling_mode="cls")
+model = SentenceTransformer(modules=[embedding_model, pooling])
 
 ds.set_format(type="torch", columns=["embeddings"], output_all_columns=True)
 
@@ -147,13 +158,13 @@ for hit in hits[0]:
     chunk = ds_flat[hit['corpus_id']]['chunk']
     print(f"[{hit['score']:0.2f}] {textwrap.shorten(chunk, width=100, placeholder='…')} [{title}]")
 
-# [0.58] Dynamika Fyzikální zákony [Newtonovy pohybové zákony]
-# [0.53] Teorie množin [Ordinální číslo]
+# [0.72] Molekulová fyzika ( též molekulární fyzika ) je část fyziky, která zkoumá látky na úrovni atomů a… [Molekulová fyzika]
+# [0.70] Fyzika ( z řeckého φυσικός ( fysikos ): přírodní, ze základu φύσις ( fysis ): příroda, archaicky… [Fyzika]
 # ...
 ```
 </details>
 
-The embeddings generation took about 25 minutes on an NVIDIA RTX 4090 24GB.
+The embeddings generation took about 35 minutes on an NVIDIA A100 80GB.
 
 ## License