Update README.md

README.md

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 We are thrilled to introduce a specialized collection of **68 large language models (LLMs)**, meticulously designed for the accounting and finance. The FinText models have been **pre-trained** on domain-specific historical data, addressing challenges like **look-ahead bias** and **information leakage**. These models are crafted to elevate the accuracy and depth of financial research and analysis.
 
 💡 **Key Features:**
-- **Domain-Specific Training:** FinText utilises diverse financial datasets such as news articles, regulatory filings, IP records, corporate speeches (ECB, FED), and more.
+- **Domain-Specific Training:** FinText utilises diverse financial datasets such as news articles, regulatory filings, IP records, key information, Speeches (ECB, FED), and more.
 - **Time-Period Specific Models:** Separate models are pre-trained for each year from **2007 to 2023**, ensuring the utmost precision and historical relevance.
 - **RoBERTa Architecture:** The suite includes both a **base model** with **125 million parameters** and a **smaller variant** with **51 million parameters**.
 - **Two distinct pre-training durations:** We also introduce a series of models to explore the impact of futher pre-training.