DedalusHealthCare
/

tinybert-ner-demo-en

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 ---
 library_name: transformers
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 ---
+license: other
+base_model: DedalusHealthCare/tinybert-mlm-en
+datasets:
+- DedalusHealthCare/ner_demo_en
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+language:
+- en
+tags:
+- token-classification
+- ner
+- named-entity-recognition
+- en
+- disorder_finding
 library_name: transformers
+pipeline_tag: token-classification
 ---
+# TinyBERT for Demo NER (English)
+## Model Description
+This model is a fine-tuned TinyBERT model for Named Entity Recognition (NER) of DISORDER_FINDING entities in English medical texts.
+It was fine-tuned from the [DedalusHealthCare/tinybert-mlm-en](https://huggingface.co/DedalusHealthCare/tinybert-mlm-en) masked language model using the [DedalusHealthCare/ner_demo_en](https://huggingface.co/datasets/DedalusHealthCare/ner_demo_en) dataset.
+**Base Model**: [DedalusHealthCare/tinybert-mlm-en](https://huggingface.co/DedalusHealthCare/tinybert-mlm-en)
+**Training Dataset**: [DedalusHealthCare/ner_demo_en](https://huggingface.co/datasets/DedalusHealthCare/ner_demo_en)
+**Task**: Token Classification (Named Entity Recognition)
+**Language**: English (en)
+**Entities**: DISORDER_FINDING
+**Model Format**: PYTORCH
+**Please use `max` as aggregation strategy in the NER pipeline (see example below)**.
 ## Training Details
+- **Training epochs**: 1
+- **Learning rate**: 5e-05
+- **Training batch size**: 32
+- **Evaluation batch size**: 32
+- **Max sequence length**: 256
+- **Warmup ratio**: 0.1
+- **Weight decay**: 0.01
+- **FP16**: True
+- **Gradient accumulation steps**: 2
+- **Save steps**: 50000
+- **Evaluation steps**: 50000
+- **Evaluation strategy**: steps
+- **Random seed**: 1
+- **Label all tokens**: True
+- **Balanced training**: False
+- **Chunk mode**: sliding_window
+- **Stride**: 16
+- **Max training samples**: None
+- **Max evaluation samples**: None
+- **Early stopping patience**: 0
+- **Early stopping threshold**: 0.0
+### Build Information
+- **Git Commit**: [9583c80](https://github.com/Dedalus-clinalytix/prod/commit/9583c80da9b9567b72c69d953854871a9badc139)
+## Use Case Configuration
+- **Use case name**: demo
+- **Language**: English (en)
+- **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-en",
+    tokenizer="DedalusHealthCare/tinybert-ner-demo-en",
+    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-en"
+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]]
+```
+## Model Architecture
+This model is based on the TinyBERT architecture with a token classification head for Named Entity Recognition.
+## Intended Use
+This model is intended for:
+- Named Entity Recognition in English medical texts
+- Identification of DISORDER_FINDING entities
+- Medical text processing and analysis
+- Research and development in medical NLP
+## Limitations
+- Trained specifically for English 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
+## Ethical Considerations
+- 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
+## Citation
+If you use this model, please cite:
+```bibtex
+@model{demo_en_ner_model,
+  title = {TinyBERT for Demo NER (English)},
+  author = {DH Healthcare GmbH},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/DedalusHealthCare/tinybert-ner-demo-en}
+}
+```
+## License
+This model is proprietary and owned by DH Healthcare GmbH. All rights reserved.
+## Contact
+For questions or support, please contact DH Healthcare GmbH.