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Llama-3-SEC-Base

Text Generation
Transformers
Safetensors
English
llama
large_language_model
finance
sec_data
continual_pre_training
model_merging
text-generation-inference
Inference Endpoints
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@@ -44,11 +44,11 @@ To ensure the robustness and effectiveness of Llama-3-SEC, the model has undergo
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  - Domain-specific perplexity, measuring the model's performance on SEC-related data
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- <img src="https://i.ibb.co/xGHRfLf/Screenshot-2024-06-11-at-10-23-59-PM.png" width="400">
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  - Extractive numerical reasoning tasks, using subsets of TAT-QA and ConvFinQA datasets
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- <img src="https://i.ibb.co/2v6PdDx/Screenshot-2024-06-11-at-10-25-03-PM.png" width="400">
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  - General evaluation metrics, such as BIG-bench, AGIEval, GPT4all, and TruthfulQA, to assess the model's performance on a wide range of tasks
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  - Domain-specific perplexity, measuring the model's performance on SEC-related data
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+ <img src="https://i.ibb.co/xGHRfLf/Screenshot-2024-06-11-at-10-23-59-PM.png" width="600">
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  - Extractive numerical reasoning tasks, using subsets of TAT-QA and ConvFinQA datasets
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+ <img src="https://i.ibb.co/2v6PdDx/Screenshot-2024-06-11-at-10-25-03-PM.png" width="600">
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  - General evaluation metrics, such as BIG-bench, AGIEval, GPT4all, and TruthfulQA, to assess the model's performance on a wide range of tasks
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