tonic-arxiv

README.md

@@ -1,23 +1,47 @@
 ---
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: authors
+    dtype: string
+  - name: published_date
+    dtype: string
+  - name: link
+    dtype: string
+  - name: markdown
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 6952989384
+    num_examples: 138380
+  download_size: 3232936300
+  dataset_size: 6952989384
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 license: cc-by-nc-sa-4.0
-size_categories:
-- 10K<n<100K
 ---
-
 ## Arxiver Dataset
-Arxiver consists of 63,357 [arXiv](https://arxiv.org/) papers converted to multi-markdown (**.mmd**) format. Our dataset includes original arXiv article IDs, titles, abstracts, authors, publication dates, URLs and corresponding markdown files published between January 2023 and October 2023.
+Arxiver consists of 138,830 [arXiv](https://arxiv.org/) papers converted to multi-markdown (**.mmd**) format. Our dataset includes original arXiv article IDs, titles, abstracts, authors, publication dates, URLs and corresponding markdown files published between January 2023 and October 2023.
 
 We hope our dataset will be useful for various applications such as semantic search, domain specific language modeling, question answering and summarization.
 
 ## Curation
-The Arxiver dataset is created using a neural OCR - [Nougat](https://facebookresearch.github.io/nougat/). After OCR processing, we apply custom text processing steps to refine the data. This includes extracting author information, removing reference sections, and performing additional cleaning and formatting. Please refer to our GitHub [repo](https://github.com/neuralwork/arxiver) for details.
+The Arxiver dataset is created using a neural OCR - [Nougat](https://facebookresearch.github.io/nougat/). After OCR processing, we apply custom text processing steps to refine the data. This includes extracting author information, removing reference sections, and performing additional cleaning and formatting.
 
 ## Using Arxiver
 You can easily download and use the arxiver dataset with Hugging Face's [datasets](https://huggingface.co/datasets) library.
 ```py
 from datasets import load_dataset
 
-# whole dataset takes 1.44GB
+# whole dataset takes 3.3GB
 dataset = load_dataset("neuralwork/arxiver") 
 print(dataset)
 ```

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