reformatting

README.md

@@ -19,15 +19,15 @@ license: mit
 - `language`: The language of the text content, and the predicted confidence of said language.
 
 
-## Pre-Processing
+# Pre-Processing
 
 CDB Cleaned is pre-processed with [nemo-curator](https://github.com/NVIDIA-NeMo/Curator). We processed the text with the following steps: 
 
 
 
-### Filtering
+## Filtering
 
-#### Text-Cleaning
+### Text-Cleaning
 
 * `UnicodeReformatter`: Uses [ftfy](https://ftfy.readthedocs.io/en/latest/) to fix broken Unicode characters.
 
@@ -39,14 +39,14 @@ CDB Cleaned is pre-processed with [nemo-curator](https://github.com/NVIDIA-NeMo/
 
 * `Fuzzy Deduplication`: Uses MinHash and Locality Sensitive Hashing to find and remove near-duplicated documents. 
 
-### Labelling
+## Labelling
 
-#### Language Management
+### Language Management
 
 * `FastText Language ID`: To label multilingual content at scale we utilize the [FastText](https://fasttext.cc/docs/en/language-identification.html) language identification model.
 
 
-#### Quality Labelling
+### Quality Labelling
 
 * `FastText Quality Filtering`: We train our own quality filterer here, and label each row with a quality rating.
 
@@ -96,8 +96,6 @@ CDB has quite a diverse distribution of languages, overall there is about 143 la
 
 
 
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 ## Tokens
 
 
@@ -107,9 +105,9 @@ Using [xlm-roberta-base](https://huggingface.co/docs/transformers/model_doc/xlm-
 
 ### Token-length distribution
 
-Computed over a sample of **2,164,574 texts** from CDB-Dec, Oct, Nov.
+Computed over a sample of **2,164,574 domains** from CDB-Dec, Oct, Nov.
 
-| Statistic | Value   |
+| Statistic | Tokens   |
 |-----------|---------|
 | Min       | 3       |
 | Max       | 702,667 |
@@ -147,12 +145,7 @@ On average we will have
 Overall across CDB-Dec, CDB-Nov, CDB-Oct 
 
 
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+# CDB v. OGB-MAG240M