Super-squash branch 'main' using huggingface_hub

.gitattributes

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+*.pcm filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+- config_name: text-only
+  data_files:
+  - split: train
+    path: text-only/train-*
+dataset_info:
+- config_name: default
+  features:
+  - name: url
+    dtype: string
+  - name: text
+    dtype: string
+  - name: date
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 4467051029
+    num_examples: 1820241
+  download_size: 1772035124
+  dataset_size: 4467051029
+- config_name: text-only
+  features:
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2305854627
+    num_examples: 1820241
+  download_size: 1360869461
+  dataset_size: 2305854627
+license: odc-by
+task_categories:
+- text-generation
+size_categories:
+- 1M<n<10M
+source_datasets: open-web-math/open-web-math
+---
+# Dataset Card for "open-web-math-minhash"
+
+An attempt at a _"high quality sample"_ of `open-web-math/open-web-math` by aggressively applying `minhash` from text-dedup. The result is 1.82M rows down from the original 6M:
+
+```
+DatasetDict({
+    train: Dataset({
+        features: ['url', 'text', 'date', 'metadata'],
+        num_rows: 1820241
+    })
+})
+```
+
+## Usage
+
+Unless you need the metadata, load the `text-only` config which is only 1.4 GB/5 shards:
+
+```python
+from datasets import load_dataset
+
+dataset_config = "text-only"
+dataset = load_dataset("BEE-spoke-data/open-web-math-minhash", dataset_config)
+```
+
+## making of
+
+On a high-RAM colab TPU (40 cores)
+
+```python
+from pathlib import Path
+from tqdm.auto import tqdm
+
+ds_name = "open-web-math/open-web-math"
+dataset_config = "default"
+data_split = 'train'
+text_column = 'text'
+
+out_dir = Path(f"output/minhash/{ds_short_name}/{data_split}")
+!mkdir -p $out_dir
+
+
+!python -m text_dedup.minhash \
+  --path $ds_name \
+  --name $dataset_config \
+  --split $data_split \
+  --cache_dir "./cache" \
+  --output $out_dir \
+  --column $text_column \
+  --ngram 5 --threshold 0.5 \
+  --hash_func xxh3 --hash_bits 16 --num_perm 64 \
+  --batch_size 10000
+
+print(f"output dir is:\n\t{out_dir}")
+!ls $out_dir
+```
+
+Console:
+
+```sh
+Resolving data files: 100% 114/114 [00:11<00:00,  9.79it/s]
+Fingerprinting... (num_proc=40): 100% 6315233/6315233 [15:27<00:00, 6806.11 examples/s]
+Iterating MinHashes...: 100% 632/632 [05:37<00:00,  1.87it/s]
+Clustering...: 100% 14/14 [01:13<00:00,  5.22s/it]
+Finding clusters... (num_proc=40): 100% 6315233/6315233 [10:57<00:00, 9602.90 examples/s]
+Filtering clusters... (num_proc=40): 100% 6315233/6315233 [03:53<00:00, 27069.61 examples/s]
+Saving the dataset (33/33 shards): 100% 1820241/1820241 [07:07<00:00, 4260.38 examples/s]
+[10/11/23 23:41:46] INFO     Loading                         :
+```
+
+
+
+
+## citation
+
+```
+@misc{paster2023openwebmath,
+      title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text}, 
+      author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba},
+      year={2023},
+      eprint={2310.06786},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI}
+}
+```

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