Improve dataset card

README.md

@@ -30,25 +30,33 @@ size_categories:
 - 100B<n<1T
 ---
 
-# Falcon RefinedWeb
+# 📀 Falcon RefinedWeb
 
 **Falcon RefinedWeb is a massive English web dataset built by [TII](https://www.tii.ae) and released under an Apache 2.0 license.**
 
-RefinedWeb is built through stringent filtering and large-scale deduplication of CommonCrawl; we found models trained on RefinedWeb to achieve performance in-line or better than models trained on curated datasets, while relying only on web data. 
+*Paper coming soon 😊.*
 
-This public extract should contain 500-650GT depending on your tokenizer of choice.
+RefinedWeb is built through stringent filtering and large-scale deduplication of CommonCrawl; we found models trained on RefinedWeb to achieve performance in-line or better than models trained on curated datasets, while only relying on web data. 
+
+This public extract should contain 500-650GT depending on the tokenizer you use, and can be enhanced with the curated corpora of your choosing.
 
 ```python
 from datasets import load_dataset
 rw = load_dataset("tiiuae/falcon-refinedweb")
 ```
 
-# Dataset card
+RefinedWeb is the main dataset we have used for training the [Falcon LLM](https://falconllm.tii.ae) models:
+
+* It was used in conjunction with a curated corpora to train Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b), two state-of-the-art open-source models.
+* It was also used to train Falcon-RW-[1B](https://huggingface.co/tiiuae/falcon-rw-1b)/[7B](https://huggingface.co/tiiuae/falcon-rw-7b), two models trained on 350 billion tokens of RefinedWeb alone to demonstrate its quality compared to curated corpora.
+
+
+# Dataset card for Falcon RefinedWeb
 
 ## Dataset Description
 
 * **Homepage:** [falconllm.tii.ae](falconllm.tii.ae)
-* **Paper:** coming soon
+* **Paper:** coming soon 😊
 * **Point of Contact:** [falconllm@tii.ae](mailto:falconllm@tii.ae)
 
 ### Dataset Summary
@@ -57,44 +65,79 @@ Falcon RefinedWeb was created to serve as an English large-scale dataset for the
 
 It was built on top of CommonCrawl, leveraging stringent filtering and extensive deduplication.
 
+### Supported Tasks and Leaderboards
+
+RefinedWeb is intended to be primarly used as a pretraining dataset for large language models. Practitioners may leverage it for upstream evaluation with a validation loss, but we do not provide any canonical split.
+
+### Languages
+
+RefinedWeb primarly contains English. 
+
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-RefinedWeb is built on-top of CommonCrawl, using the Macrodata Refinement Pipeline, which combines content extraction, filtering heuristics, and deduplication. 
+Falcon RefinedWeb is built on-top of [CommonCrawl](https://commoncrawl.org), using the Macrodata Refinement Pipeline, which combines content extraction, filtering heuristics, and deduplication. 
 
 In designing RefinedWeb, we abided to the following philosophy: 
 
-* (1) **Scale first.** We intend MDR to produce datasets to be used to train 40-200B parameters models, thus requiring trillions of tokens For English-only RefinedWeb, we target a size of 3-6 trillion tokens. Specifically, we eschew any labour intensive human curation process, and focus on CommonCrawl instead of disparate single-domain sources.
-* (2) **Strict deduplication.** Inspired by the work of Katherine Lee, which demonstrated the value of deduplication for large language models, we implement a rigorous deduplication pipeline. We combine both exact and fuzzy deduplication, and use strict settings leading to removal rates far higher than others have reported.
-* (3) **Neutral filtering.** To avoid introducing further undesirable biases into the model, we avoid using ML-based filtering outside of language identification. We stick to simple rules and heuristics, and use only URL filtering for adult content.
+* (1) **Scale first.** We intend MDR to produce datasets to be used to train 40-200B parameters models, thus requiring trillions of tokens [(Hoffmann et al., 2022)](https://arxiv.org/abs/2203.15556). For English-only RefinedWeb, we target a size of 3-6 trillion tokens. Specifically, we eschew any labour intensive human curation process, and focus on CommonCrawl instead of disparate single-domain sources.
+* (2) **Strict deduplication.** Inspired by the work of [Lee et al., 2021](https://arxiv.org/abs/2107.06499), which demonstrated the value of deduplication for large language models, we implement a rigorous deduplication pipeline. We combine both exact and fuzzy deduplication, and use strict settings leading to removal rates far higher than others datasets have reported.
+* (3) **Neutral filtering.** To avoid introducing further undesirable biases into the model, we avoid using ML-based filtering outside of language identification ([Dodge et al., 2021](https://arxiv.org/abs/2104.08758); [Welbl et al., 2021](https://arxiv.org/abs/2109.07445)) . We stick to simple rules and heuristics, and use only URL filtering for adult content.
 
-During its development, we iterated on RefinedWeb by measuring the zero-shot performance of models trained with earlier versions of the dataset. We also manually audited samples to identify potential filtering improvements.
+During its development, we iterated on RefinedWeb by measuring the zero-shot performance of models trained on development version of the dataset. Our main goal was to maximize the performance obtained, bridging the gap between curated and web data. We also manually audited samples to identify potential filtering improvements.
 
 ### Source Data
 
-RefinedWeb is built from [CommonCrawl](https://commoncrawl.org) dumps. 
+RefinedWeb is built from [CommonCrawl](https://commoncrawl.org) dumps. These dumps are constructed from crawling publicly available web pages. 
 
 ### Data Collection and Preprocessing
 
-See the upcoming paper for further details.
+We applied extensive preprocessing and cleaning of the data, using our Macrodata Refinement Pipeline. 
 
-We applied extensive preprocessing and cleaning of the data. 
+We first filter URLs to remove adult content using a blocklist and a score system, we then use `trafilatura` to extract content from pages, and perform language identification with the `fastText` classifier from CCNet ([Wenzek et al., 2019](https://arxiv.org/abs/1911.00359)). After this first preprocessing stage, we filter data using heuristics from MassiveWeb ([Rae et al., 2021](https://arxiv.org/abs/2112.11446)), and our own line-wise corrections. 
 
-We first filter URLs to remove adult content using a blocklist and a score system, we then use `trafilatura` to extract content from pages, and perform language identification with the `fastText` classifier from CCNet. 
+Finally, we run extensive deduplication, removing URLs revisited across dumps and performing subsequently fuzzy and exact substring deduplication.
 
-After this first preprocessing stage, we filter data using heuristics from MassiveWeb, and our own line-wise corrections. 
+### Annotations
 
-Finally, we run extensive deduplication, removing URLs revisited across dumps and performing subsequently fuzzy and exact substring deduplication.
+We provide automatically collected annotations for the source `url`, `timestamp` of the crawl, original CommonCrawl `dump` and `segment` in which the document was found, and `image_urls` contained in the page.
+
+
+### Personal and Sensitive Information
 
+As RefinedWeb is built upon publicly available web pages, it may contain sensitive information such as emails, phone numbers, or IP addresses. We believe that deduplication may have helped reduced the prevalence of PII in the dataset, but practitioners working with RefinedWeb should take care.
 
 ## Considerations for Using the Data
 
-Despite our best efforts to filter content that does not qualify as natural language, and to deduplicate documents, our pipeline may let through documents that may be considered as errors or redundant.
+### Social Impact of Dataset
+
+With the open-source release of Falcon RefinedWeb, we aim to increase access to high-quality web data, which has typically been held private by model developers. We believe this release will in turn improve the accessibility and the spread of performant large language models.  
+
+### Discussion of Biases
 
 As toxic or biased data is prevalent on the internet, it is likely our dataset contains such content. Notably, using the Perspective API, we estimated the prevalence of toxic content in the dataset to be similar to The Pile.
 
+### Other Known Limitations
+
+Despite our best efforts to filter content that does not qualify as natural language, and to deduplicate documents, our pipeline may let through documents that may be considered as errors or redundant.
+
 ## Additional Information
 
 ### Licensing Information
@@ -103,4 +146,4 @@ Apache 2.0.
 
 ### Citation Information
 
-Paper coming soon. 
+Paper coming soon 😊.