Update README.md

README.md

@@ -1,11 +1,11 @@
 ---
 # Model Card generated based on AutoTrain run
-# Date: 2025-04-05 (Please update with actual date)
+# Date: 2025-04-07
 language:
 - en # Primarily English from EDGAR
-- multilingual # Corrected special value
+- multilingual # Assumed multilingual from European sources & XLM-R base
 library_name: transformers
-license: apache-2.0 # Or appropriate license if you choose one
+license: apache-2.0 # Or appropriate license
 tags:
 - text-classification
 - financial-filings
@@ -19,7 +19,7 @@ widget:
 datasets:
 - custom # Combined Labelbox and EDGAR data
 model-index:
-- name: xlm-roberta-large-fin-filing-classification # Example Name
+- name: FinancialReports/filing-classification-xlmr # Model Repo ID
   results:
   - task:
       type: text-classification
@@ -28,37 +28,36 @@ model-index:
       type: custom
       name: Combined Financial Filings (Labelbox + EDGAR)
       split: validation
-    # Corrected metrics format (array of objects, removed config object)
     metrics:
       - type: accuracy
         value: 0.9617
         name: Accuracy
       - type: f1
         value: 0.6470
-        name: F1 (Macro) # Averaging specified in name
+        name: F1 (Macro)
       - type: f1
         value: 0.9597
-        name: F1 (Weighted) # Averaging specified in name
+        name: F1 (Weighted)
       - type: loss
         value: 0.1687
         name: Loss
 ---
 
-# Model Card: XLM-RoBERTa-Large Financial Filing Classifier
+# Model Card: FinancialReports Filing Classifier
 
 ## Model Details
 
-* **Model Name:** `xlm-roberta-large-fin-filing-classification` (Example - Replace with your chosen Hub repo name)
+* **Model Name:** `FinancialReports/filing-classification-xlmr` (Assumed Repo ID based on AutoTrain project & org)
 * **Description:** This model is a fine-tuned version of `FacebookAI/xlm-roberta-large` designed for multi-class text classification of financial filing documents. It classifies input text (expected in markdown format) into one of 37 predefined filing type categories.
 * **Base Model:** [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large)
-* **Developed by:** [Your Name/Organization - e.g., silashundhausen]
-* **Model Version:** 1.0 (Example)
+* **Developed by:** FinancialReports ([financialreports.eu](https://financialreports.eu))
+* **Model Version:** 1.0
 * **Fine-tuning Framework:** Hugging Face AutoTrain
 
 ## Intended Use
 
 * **Primary Use:** To automatically classify financial filing documents based on their textual content into one of 37 categories (e.g., Annual Report, Quarterly Report, Directors' Dealings, etc.).
-* **Primary Users:** Financial analysts, data providers, regulatory compliance teams, researchers.
+* **Primary Users:** Financial analysts, data providers, regulatory compliance teams, researchers associated with FinancialReports.
 * **Out-of-Scope Uses:** This model is not designed for sentiment analysis, named entity recognition, or classification tasks outside the defined 37 financial filing types. Performance on filing types significantly different from those in the training data is not guaranteed.
 
 ## Training Data
@@ -113,8 +112,8 @@ You can use this model via the Hugging Face `transformers` library:
 ```python
 from transformers import pipeline
 
-# Load the classifier pipeline (replace with your actual model repo ID)
-model_repo_id = "silashundhausen/filing-classification-xlmr" # Example ID
+# Load the classifier pipeline (replace with your actual model repo ID on the Hub)
+model_repo_id = "FinancialReports/filing-classification-xlmr"
 classifier = pipeline("text-classification", model=model_repo_id)
 
 # Example usage
@@ -144,12 +143,11 @@ print(predictions)
 # results = [{"label": model.config.id2label[i], "score": prob.item()} for i, prob in enumerate(probabilities)]
 # results.sort(key=lambda x: x["score"], reverse=True)
 # print(results)
-
-Citation@misc{your_model_citation_tag, # Consider creating one
-  author    = {[Your Name/Organization]},
+Citation@misc{financialreports_filing_classifier_2025,
+  author    = {FinancialReports},
   title     = {XLM-RoBERTa-Large Financial Filing Classifier},
   year      = {2025},
   publisher = {Hugging Face},
   journal   = {Hugging Face Model Hub},
-  howpublished = {\url{[https://huggingface.co/](https://huggingface.co/)[your-username]/[your-repo-name]}}, # Replace URL
+  howpublished = {\url{[https://huggingface.co/FinancialReports/filing-classification-xlmr](https://www.google.com/search?q=https://huggingface.co/FinancialReports/filing-classification-xlmr)}}, # Assumed URL
 }