Update README.md

README.md

@@ -131,7 +131,7 @@ The dataset was curated by [Loukas et al. (2022)](https://arxiv.org/abs/2203.064
 FiNER-139 is compiled from approx. 10k annual and quarterly English reports (filings) of publicly traded companies downloaded from the [US Securities
 and Exchange Commission's (SEC)](https://www.sec.gov/) [Electronic Data Gathering, Analysis, and Retrieval (EDGAR)](https://www.sec.gov/edgar.shtml) system. <br>
 The reports span a 5-year period, from 2016 to 2020. They are annotated with XBRL tags by professional auditors and describe the performance and projections of the companies. XBRL defines approx. 6k entity types from the US-GAAP taxonomy. FiNER-139 is annotated with the 139 most frequent XBRL entity types with at least 1,000 appearances. <br>
-We used regular expressions to extract the text notes from the Financial Statements Item of each filing, which is the primary source of XBRL tags in annual and quarterly reports. We used the **IOB2** annotation scheme to distinguish tokens at the beginning, inside, or outside of tagged expressions, which leads to 279 possible token labels.
+We used regular expressions to extract the text notes from the Financial Statements Item of each filing, which is the primary source of XBRL tags in annual and quarterly reports. We used the <strong>IOB2</strong> annotation scheme to distinguish tokens at the beginning, inside, or outside of tagged expressions, which leads to 279 possible token labels.
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 ### Annotations
@@ -193,8 +193,8 @@ In the Proceedings of the 60th Annual Meeting of the Association for Computation
 <img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="SEC-BERT" width="400"/>
 
 <div style="text-align: justify">
-We also pre-train our own BERT models (**SEC-BERT**) for the financial domain, intended to assist financial NLP research and FinTech applications. <br>
-**SEC-BERT** consists of the following models:
+We also pre-train our own BERT models (<strong>SEC-BERT</strong>) for the financial domain, intended to assist financial NLP research and FinTech applications. <br>
+<strong>SEC-BERT</strong> consists of the following models:
 
 * [**SEC-BERT-BASE**](https://huggingface.co/nlpaueb/sec-bert-base): Same architecture as BERT-BASE trained on financial documents.
 * [**SEC-BERT-NUM**](https://huggingface.co/nlpaueb/sec-bert-num): Same as SEC-BERT-BASE but we replace every number token with a [NUM] pseudo-token handling all numeric expressions in a uniform manner, disallowing their fragmentation