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@@ -26,7 +26,7 @@ We are thrilled to introduce a specialized collection of **68 large language mod
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  - **Domain-Specific Training:** FinText utilises diverse financial datasets such as news articles, regulatory filings, IP records, corporate speeches (ECB, FED), and more.
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  - **Time-Period Specific Models:** Separate models are pre-trained for each year from **2007 to 2023**, ensuring the utmost precision and historical relevance.
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  - **RoBERTa Architecture:** The suite includes both a **base model** with **125 million parameters** and a **smaller variant** with **51 million parameters**.
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- - **Two distinct pre-training durations:** We also introduce a series of models to explore the impact of futher pre-training. These models are pre-trained for an additional 5 epochs, extending the total pre-training epochs to 10.
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  Stay tuned for upcoming updates and new features for FinText. We expect to launch stages 2 and 3 within the next 9 months. 🎉
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  - **Domain-Specific Training:** FinText utilises diverse financial datasets such as news articles, regulatory filings, IP records, corporate speeches (ECB, FED), and more.
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  - **Time-Period Specific Models:** Separate models are pre-trained for each year from **2007 to 2023**, ensuring the utmost precision and historical relevance.
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  - **RoBERTa Architecture:** The suite includes both a **base model** with **125 million parameters** and a **smaller variant** with **51 million parameters**.
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+ - **Two distinct pre-training durations:** We also introduce a series of models to explore the impact of futher pre-training.
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  Stay tuned for upcoming updates and new features for FinText. We expect to launch stages 2 and 3 within the next 9 months. 🎉
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