Upload PyTorch model

README.md

@@ -1,174 +1,72 @@
 ---
-license: other
-base_model: DedalusHealthCare/tinybert-mlm-en
-datasets:
-- DedalusHealthCare/ner_demo_en
-task_categories:
-- token-classification
-task_ids:
-- named-entity-recognition
+library_name: transformers
 language:
 - en
+license: other
+base_model: DedalusHealthCare/tinybert-mlm-en
 tags:
-- token-classification
-- ner
-- named-entity-recognition
-- en
-- disorder_finding
-library_name: transformers
-pipeline_tag: token-classification
+- generated_from_trainer
+datasets:
+- demo-en
+model-index:
+- name: tinybert-clinalytix_1774257349
+  results: []
 ---
 
-# 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)
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-# Get predictions
-with torch.no_grad():
-    outputs = model(**tokens)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+# tinybert-clinalytix_1774257349
 
-# Get labels
-predicted_token_class_ids = predictions.argmax(-1)
-labels = [model.config.id2label[id.item()] for id in predicted_token_class_ids[0]]
-```
+This model is a fine-tuned version of [DedalusHealthCare/tinybert-mlm-en](https://huggingface.co/DedalusHealthCare/tinybert-mlm-en) on the demo-en dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2915
+- Disorder Finding Precision: 0.0667
+- Disorder Finding Recall: 0.4
+- Disorder Finding F1: 0.1143
+- Disorder Finding Number: 5
+- Overall Precision: 0.0667
+- Overall Recall: 0.4
+- Overall F1: 0.1143
+- Overall Accuracy: 0.2222
 
-## Model Architecture
+## Model description
 
-This model is based on the TinyBERT architecture with a token classification head for Named Entity Recognition.
+More information needed
 
-## Intended Use
+## Intended uses & limitations
 
-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
+More information needed
 
-## Limitations
+## Training and evaluation data
 
-- 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
+More information needed
 
-- 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
+## Training procedure
 
-## Citation
+### Training hyperparameters
 
-If you use this model, please cite:
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 1
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 1
+- mixed_precision_training: Native AMP
+- label_smoothing_factor: 0.1
 
-```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}
-}
-```
+### Training results
 
-## License
 
-This model is proprietary and owned by DH Healthcare GmbH. All rights reserved.
 
-## Contact
+### Framework versions
 
-For questions or support, please contact DH Healthcare GmbH.
+- Transformers 4.45.1
+- Pytorch 2.6.0+cu124
+- Datasets 4.5.0
+- Tokenizers 0.20.3

all_results.json

@@ -0,0 +1,22 @@
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+    "eval_DISORDER_FINDING_number": 5,
+    "eval_DISORDER_FINDING_precision": 0.06666666666666667,
+    "eval_DISORDER_FINDING_recall": 0.4,
+    "eval_loss": 1.2915407419204712,
+    "eval_overall_accuracy": 0.2222222222222222,
+    "eval_overall_f1": 0.1142857142857143,
+    "eval_overall_precision": 0.06666666666666667,
+    "eval_overall_recall": 0.4,
+    "eval_runtime": 0.1761,
+    "eval_samples": 3,
+    "eval_samples_per_second": 17.036,
+    "eval_steps_per_second": 5.679,
+    "total_flos": 6768861120.0,
+    "train_loss": 0.6272501945495605,
+    "train_runtime": 0.752,
+    "train_samples": 15,
+    "train_samples_per_second": 19.947,
+    "train_steps_per_second": 1.33
+}

checkpoint-1/added_tokens.json

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+  "<PATIENT>": 28999,
+  "<PHONE>": 29000,
+  "<VENDOR>": 28996
+}

checkpoint-1/config.json

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+{
+  "_name_or_path": "DedalusHealthCare/tinybert-mlm-en",
+  "architectures": [
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+  ],
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+  "finetuning_task": "ner",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 312,
+  "id2label": {
+    "0": "B-DISORDER_FINDING",
+    "1": "I-DISORDER_FINDING",
+    "2": "O"
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+  "label2id": {
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+    "I-DISORDER_FINDING": 1,
+    "O": 2
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+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 4,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "pre_trained": "",
+  "torch_dtype": "float32",
+  "training": "",
+  "transformers_version": "4.45.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 29002
+}

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checkpoint-1/tokenizer_config.json

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eval_results.json

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model_info.json

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 {
-    "model_version": 1765536980,
+    "model_version": 1774259037,
     "model_name": "tinybert-demo-en",
     "model_type": "tinybert",
     "model_platform": "pytorch",
     "model_architecture": "TinyBERT",
     "model_description": "Retrieve named entities from text.",
-    "model_date": "2025-12-12T10:56:20.815542+00:00",
-    "clinalytix_version": "25.12.0",
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+    "clinalytix_version": "26.03.0",
     "model_objective": "RECOGNITION",
     "use_case": "demo",
     "build_number": "10",
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     "language_code": "en",
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tokenizer.json

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train_results.json

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