Update model card with detailed information

README.md

@@ -1,199 +1,211 @@
 ---
+license: other
+base_model: DedalusHealthCare/tinybert-mlm-de
+datasets:
+- DedalusHealthCare/ner_demo_de
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+language:
+- de
+tags:
+- token-classification
+- ner
+- named-entity-recognition
+- de
+- disorder_finding
 library_name: transformers
-tags: []
+pipeline_tag: token-classification
 ---
 
-# Model Card for Model ID
+# TinyBERT for Demo NER (German)
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Description
 
+This model is a fine-tuned TinyBERT model for Named Entity Recognition (NER) of DISORDER_FINDING entities in German medical texts.
 
+It was fine-tuned from the [DedalusHealthCare/tinybert-mlm-de](https://huggingface.co/DedalusHealthCare/tinybert-mlm-de) masked language model using the [DedalusHealthCare/ner_demo_de](https://huggingface.co/datasets/DedalusHealthCare/ner_demo_de) dataset.
 
-## Model Details
+**Base Model**: [DedalusHealthCare/tinybert-mlm-de](https://huggingface.co/DedalusHealthCare/tinybert-mlm-de)
 
-### Model Description
+**Training Dataset**: [DedalusHealthCare/ner_demo_de](https://huggingface.co/datasets/DedalusHealthCare/ner_demo_de)
 
-<!-- Provide a longer summary of what this model is. -->
+**Task**: Token Classification (Named Entity Recognition)
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+**Language**: German (de)
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [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]
+**Entities**: DISORDER_FINDING
 
-### Model Sources [optional]
+**Model Format**: PYTORCH+ONNX
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- 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]
+**Please use `max` as aggregation strategy in the NER pipeline (see example below)**.
 
 ## 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] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### 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]
+- **Training epochs**: 1
+- **Learning rate**: N/A
+- **Training batch size**: 32
+- **Evaluation batch size**: 32
+- **Max sequence length**: 256
+- **Warmup steps**: N/A
+- **FP16**: False
+- **Gradient accumulation steps**: 2
+- **Evaluation accumulation steps**: 2
+- **Save steps**: 15000
+- **Evaluation steps**: 10000
+- **Evaluation strategy**: steps
+- **Random seed**: 33
+- **Label all tokens**: True
+- **Balanced training**: False
+- **Chunk mode**: sliding_window
+- **Stride**: 16
+- **Max training samples**: None
+- **Max evaluation samples**: 10000
+- **Early stopping patience**: 0
+- **Early stopping threshold**: 0.0
+
+## Use Case Configuration
+
+- **Use case name**: demo
+- **Language**: German (de)
+- **Target entities**: DISORDER_FINDING
+- **Text processing max length**: N/A
+- **Entity labeling scheme**: N/A
+
+## Usage
+
+### Using Transformers Pipeline
+
+```python
+from transformers import pipeline
+
+# Load the model
+ner_pipeline = pipeline(
+    "ner",
+    model="DedalusHealthCare/tinybert-ner-demo-de",
+    tokenizer="DedalusHealthCare/tinybert-ner-demo-de",
+    aggregation_strategy="max"
+)
+
+# Example text
+text = "Der Patient hat Diabetes und Bluthochdruck."
+
+# Get predictions
+entities = ner_pipeline(text)
+print(entities)
+```
+
+### Using AutoModel and AutoTokenizer
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+import torch
+
+# Load model and tokenizer
+model_name = "DedalusHealthCare/tinybert-ner-demo-de"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+# Tokenize text
+text = "Der Patient hat Diabetes und Bluthochdruck."
+tokens = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+# Get predictions
+with torch.no_grad():
+    outputs = model(**tokens)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+# Get labels
+predicted_token_class_ids = predictions.argmax(-1)
+labels = [model.config.id2label[id.item()] for id in predicted_token_class_ids[0]]
+```
+
+### Using ONNX Runtime (Optimized Inference)
+
+```python
+from optimum.onnxruntime import ORTModelForTokenClassification
+from transformers import AutoTokenizer, pipeline
+import torch
+
+# Load ONNX model for faster inference
+model_name = "DedalusHealthCare/tinybert-ner-demo-de"
+onnx_model = ORTModelForTokenClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Create pipeline with ONNX model (recommended)
+ner_pipeline = pipeline(
+    "ner",
+    model=onnx_model,
+    tokenizer=tokenizer,
+    aggregation_strategy="max"
+)
+
+# Example text
+text = "Der Patient hat Diabetes und Bluthochdruck."
+entities = ner_pipeline(text)
+print(entities)
+
+# Direct model usage
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+with torch.no_grad():
+    outputs = onnx_model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+predicted_token_class_ids = predictions.argmax(-1)
+token_labels = [onnx_model.config.id2label[id.item()] for id in predicted_token_class_ids[0]]
+```
 
-#### Software
+### Performance Comparison
 
-[More Information Needed]
+- **PyTorch**: Standard format, suitable for training and research
+- **ONNX**: Optimized for inference, typically 2-4x faster than PyTorch
+- **Recommendation**: Use ONNX for production inference, PyTorch for research
 
-## Citation [optional]
+## Model Architecture
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+This model is based on the TinyBERT architecture with a token classification head for Named Entity Recognition.
 
-**BibTeX:**
+## Intended Use
 
-[More Information Needed]
+This model is intended for:
+- Named Entity Recognition in German medical texts
+- Identification of DISORDER_FINDING entities
+- Medical text processing and analysis
+- Research and development in medical NLP
 
-**APA:**
+## Limitations
 
-[More Information Needed]
+- Trained specifically for German medical texts
+- Performance may vary on texts from different medical domains
+- May not generalize well to non-medical texts
+- Requires careful evaluation on new datasets
 
-## Glossary [optional]
+## Ethical Considerations
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+- This model is trained on medical data and should be used responsibly
+- Outputs should be validated by medical professionals
+- Patient privacy and data protection regulations must be followed
+- The model may have biases present in the training data
 
-[More Information Needed]
+## Citation
 
-## More Information [optional]
+If you use this model, please cite:
 
-[More Information Needed]
+```bibtex
+@model{demo_de_ner_model,
+  title = {TinyBERT for Demo NER (German)},
+  author = {DH Healthcare GmbH},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/DedalusHealthCare/tinybert-ner-demo-de}
+}
+```
 
-## Model Card Authors [optional]
+## License
 
-[More Information Needed]
+This model is proprietary and owned by DH Healthcare GmbH. All rights reserved.
 
-## Model Card Contact
+## Contact
 
-[More Information Needed]
+For questions or support, please contact DH Healthcare GmbH.