Alijeff1214
/

DeutscheLexAI_BGB

@@ -1,237 +1,251 @@
----
-library_name: transformers
-tags:
-- unsloth
-- trl
-- grpo
-license: apache-2.0
-base_model:
-- Qwen/Qwen2.5-3B
-- Qwen/Qwen2.5-VL-3B-Instruct
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-DeutscheLexAI_BGB is a fine-tuned Qwen2.5-3B model specializing in German legal text processing, trained on the Bürgerliches Gesetzbuch (BGB) dataset. It enhances legal text understanding, summarization, and reasoning for German legal documents.
-- **Developed by:** [Ali Asghar (jaffry258@gmail.com)]
-- **Funded by [optional]:** [still under progress ]
-- **Shared by [optional]:** []
-- **Model type:** [Large Language Model (LLM)]
-- **Language(s) (NLP):** [pytorch,transformers,python]
-- **License:** [Appache 2.0]
-- **Finetuned from model [optional]:** [Qwen2.5-3B]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB/tree/main]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-DeutscheLexAI_BGB is a fine-tuned Qwen2.5-3B model specializing in German legal text processing, trained on the Bürgerliches Gesetzbuch (BGB) dataset. It enhances legal text understanding, summarization, and reasoning for German legal documents.
-### Direct Use
-Legal research: Extract, summarize, and analyze BGB texts.
-AI-powered legal assistants: Provide insights into German law.
-Academic purposes: Assists in legal document structuring.
-[More Information Needed]
-### Downstream Use [optional]
-Chatbots for legal guidance.
-AI-based contract analysis.
-[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
-The model may reflect biases in the BGB dataset.
-Not suitable for real-time legal decision-making.
-Might struggle with non-German legal texts.
-[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 Dataset 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 Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed]
-- trainer = GRPOTrainer(
-    model = model,
-    processing_class = tokenizer,
-    reward_funcs = [
-        xmlcount_reward_func,
-        soft_format_reward_func,
-        strict_format_reward_func,
-        int_reward_func,
-        correctness_reward_func,
-    ],
-    args = training_args,
-    train_dataset = dataset,
-)
-trainer.train()
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset 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
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-@article{DeutscheLexAI_BGB,
-  title={DeutscheLexAI_BGB: A Fine-Tuned Qwen2.5-3B Model for German Legal Texts},
-  author={Your Name or Organization},
-  journal={Hugging Face Model Hub},
-  year={2025},
-  url={https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB}
-}
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
 [More Information Needed]

+---
+library_name: transformers
+tags:
+- unsloth
+- trl
+- grpo
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-3B
+- Qwen/Qwen2.5-VL-3B-Instruct
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+# Model Card for Model ID
+<!-- Provide a quick summary of what the model is/does. -->
+## Model Details
+### Model Description
+<!-- Provide a longer summary of what this model is. -->
+DeutscheLexAI_BGB is a fine-tuned Qwen2.5-3B model specializing in German legal text processing, trained on the Bürgerliches Gesetzbuch (BGB) dataset. It enhances legal text understanding, summarization, and reasoning for German legal documents.
+- **Developed by:** [Ali Asghar (jaffry258@gmail.com)]
+- **Funded by [optional]:** [still under progress ]
+- **Shared by [optional]:** []
+- **Model type:** [Large Language Model (LLM)]
+- **Language(s) (NLP):** [pytorch,transformers,python]
+- **License:** [Appache 2.0]
+- **Finetuned from model [optional]:** [Qwen2.5-3B]
+### Model Sources [optional]
+<!-- Provide the basic links for the model. -->
+- **Repository:** [https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB/tree/main]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+## Uses
+DeutscheLexAI_BGB is a fine-tuned Qwen2.5-3B model specializing in German legal text processing, trained on the Bürgerliches Gesetzbuch (BGB) dataset. It enhances legal text understanding, summarization, and reasoning for German legal documents.
+### Direct Use
+Legal research: Extract, summarize, and analyze BGB texts.
+AI-powered legal assistants: Provide insights into German law.
+Academic purposes: Assists in legal document structuring.
+[More Information Needed]
+### Downstream Use [optional]
+Chatbots for legal guidance.
+AI-based contract analysis.
+[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
+The model may reflect biases in the BGB dataset.
+Not suitable for real-time legal decision-making.
+Might struggle with non-German legal texts.
+[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 Dataset 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 Procedure
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+#### Preprocessing [optional]
+[More Information Needed]
+#### Training Hyperparameters
+- **Training regime:** [More Information Needed]
+- trainer = GRPOTrainer(
+    model = model,
+    processing_class = tokenizer,
+    reward_funcs = [
+        xmlcount_reward_func,
+        soft_format_reward_func,
+        strict_format_reward_func,
+        int_reward_func,
+        correctness_reward_func,
+    ],
+    args = training_args,
+    train_dataset = dataset,
+)
+trainer.train()
+#### Speeds, Sizes, Times [optional]
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+[More Information Needed]
+## Evaluation
+<!-- This section describes the evaluation protocols and provides the results. -->
+### Testing Data, Factors & Metrics
+#### Testing Data
+<!-- This should link to a Dataset 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
+## Model Examination [optional]
+<!-- Relevant interpretability work for the model goes here -->
+[More Information Needed]
+## Environmental Impact
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+## Technical Specifications [optional]
+### Model Architecture and Objective
+[More Information Needed]
+### Compute Infrastructure
+[More Information Needed]
+#### Hardware
+[More Information Needed]
+#### Software
+[More Information Needed]
+## Citation [optional]
+@article{DeutscheLexAI_BGB,
+  title={DeutscheLexAI_BGB: A Fine-Tuned Qwen2.5-3B Model for German Legal Texts},
+  author={Your Name or Organization},
+  journal={Hugging Face Model Hub},
+  year={2025},
+  url={https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB}
+}
+**BibTeX:**
+[More Information Needed]
+**APA:**
+[More Information Needed]
+## Glossary [optional]
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+[More Information Needed]
+## More Information [optional]
+[More Information Needed]
+## Model Card Authors [optional]
+[More Information Needed]
+## Model Card Contact
 [More Information Needed]