Update README.md

README.md

@@ -28,27 +28,25 @@ widget:
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model performs entity extraction and relation extraction in a combined manner, using __*entity markers*__ and __*task triggers*__.
+It processes German tax laws as input and outputs the extracted key figures with their properties and relations, based on a developed semantic model.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This is a fine-tuned token classification model, based on XLM-RoBERTa-Large, for the extraction of key figures and their logical connected properties from tax legal texts. The entity- and relation extraction tasks are trained in a combined model using initial trigger token to distinguish between the tasks. For relation extraction additional tokens are used to mark the extracted entities and predict the relations between them.
 
 
-- **Developed by:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Model type:** fine-tuned token classification model, based on XLM-RoBERTa-Large
+- **Language(s) (NLP):** German
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
 - **Repository:** https://github.com/danielsteinigen/nlp-legal-texts
-- **Paper:** [More Information Needed]
+- **Paper:** https://ceur-ws.org/Vol-3441/paper7.pdf
 - **Demo:** https://huggingface.co/spaces/danielsteinigen/NLP-Legal-Texts
 
 ## Uses
@@ -82,91 +80,18 @@ print(results_relations_4)
 
 
 ```
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+Training details can be found in our paper: [https://ceur-ws.org/Vol-3441/paper7.pdf](https://ceur-ws.org/Vol-3441/paper7.pdf)
 
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Training Data
 
+The model is trained on our dataset __*KeyFiTax*__, which is published here:[https://huggingface.co/datasets/danielsteinigen/KeyFiTax](https://huggingface.co/datasets/danielsteinigen/KeyFiTax)
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
+Evaluation details can be found in our paper: [https://ceur-ws.org/Vol-3441/paper7.pdf](https://ceur-ws.org/Vol-3441/paper7.pdf)
 
 
 ## Citation [optional]
@@ -174,13 +99,23 @@ Use the code below to get started with the model.
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
+```
+@inproceedings{steinigen2023semantic,
+  title={Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models},
+  author={Steinigen, Daniel and Namysl, Marcin and Hepperle, Markus and Krekeler, Jan and Landgraf, Susanne},
+  url = {https://ceur-ws.org/Vol-3441/paper7.pdf},
+  year={2023}
+  journal={Sixth Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2023)}, 
+  series = {CEUR Workshop Proceedings},
+  venue = {Braga, Portugal},
+  eventdate = {2023-06-23}
+}
 
-[More Information Needed]
+```
 
 **APA:**
 
-[More Information Needed]
-
-## Model Card Contact
+Steinigen, D., Namysl, M., Hepperle, M., Krekeler, J., & Landgraf, S. (2023). Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models.
+Proceedings of Sixth Workshop on Automated Semantic Analysis of Information in Legal Text, Braga, Portugal, June 23, 2023,
+CEUR-WS.org, online CEUR-WS.org/Vol-3441/paper7.pdf.
 
-[More Information Needed]