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--- |
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language: |
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- en |
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tags: |
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- financial NLP |
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- named entity recognition |
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- sequence labeling |
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- structured extraction |
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- hierarchical taxonomy |
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- XBRL |
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- iXBRL |
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- SEC filings |
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- financial-information-extraction |
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datasets: |
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- AAU-NLP/HiFi-KPI |
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model_name: "Pre-BERT-SL1000" |
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library_name: "transformers" |
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pipeline_tag: "token-classification" |
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base_model: "bert-base-uncased" |
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task_categories: |
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- token-classification |
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task_ids: |
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- named-entity-recognition |
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- financial-information-extraction |
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pretty_name: "Pre-BERT-SL1000: Sequence Labeling for Presentation Taxonomy KPI Extraction" |
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size_categories: "1M<n<10M" |
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languages: |
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- en |
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dataset_name: "HiFi-KPI" |
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model_description: | |
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Pre-BERT-SL1000 is a **BERT-based sequence labeling model** fine-tuned on the **HiFi-KPI dataset** for extracting |
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**financial key performance indicators (KPIs)** from **SEC earnings filings (10-K & 10-Q)**. It specializes in identifying |
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entities that are one level up the **presentation taxonomy**, such as revenueAbstract, earnings, and financial ratios, using **token classification**. |
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This model is trained specifically on n=1 with the **presentation taxonomy labels** from **HiFi-KPI**, focusing on entity identification. |
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dataset_link: "https://huggingface.co/datasets/AAU-NLP/HiFi-KPI" |
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repo_link: "https://github.com/rasmus393/HiFi-KPI" |
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--- |
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## **Pre-BERT-SL1000** |
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### **Model Description** |
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Pre-BERT-SL1000 is a **BERT-based sequence labeling model** fine-tuned on the **[HiFi-KPI dataset](https://huggingface.co/datasets/AAU-NLP/HiFi-KPI)** for extracting **financial key performance indicators (KPIs)** from **SEC earnings filings (10-K & 10-Q)**. It specializes in identifying entities, such as revenue, earnings, etc. |
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This model is trained on the [HiFi-KPI dataset](https://huggingface.co/datasets/AAU-NLP/HiFi-KPI) and is focused on the **presentation layer taxonomy** with **n=1**. |
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### **Use Cases** |
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- Extracting **financial KPIs** using **iXBRL presentation taxonomy** |
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- **Financial document parsing** with entity recognition |
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### **Performance** |
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- Trained on **1,000 most frequent labels** from the **[HiFi-KPI dataset](https://huggingface.co/datasets/AAU-NLP/HiFi-KPI)** with n=1 in the **presentation taxonomy** |
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### **Dataset & Code** |
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- **Dataset**: [HiFi-KPI on Hugging Face](https://huggingface.co/datasets/AAU-NLP/HiFi-KPI) |
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- **Code example**: [HiFi-KPI GitHub Repository](https://github.com/rasmus393/HiFi-KPI) |
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