Update README.md

README.md

@@ -11,5 +11,67 @@ library_name: transformers
 tags:
 - medical
 - healthcare
---
-Model Name: DeepNeural_NER-I
+---
+# Model Name: DeepNeural_NER-I
+
+<!-- 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. -->
+
+# Bert-base-uncased
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the medical-ner-bleurt-separated dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0
+- F1: 1.0
+
+## Model description
+
+The DeepNeural NER-I model is exclusively designed to identify body parts in textual documents. 
+This clinical support model is one of many to be released, and is a crucial aspect  of clinical support systems. 
+
+## Intended uses & limitations
+
+The model is meant to be used for research and development purposes by Data Scientists, ML & Software Engineers for the development of NER applications 
+capable of identifying body parts in medical EHR systems to augment patient health processing.
+
+## Training and evaluation data
+
+Training 
+
+## Training procedure
+The DeepNeural_NER-I model was trained with precision and accuracy in mind, and therefore 
+the model was trained for 3 epochs and 13500 global steps per epoch. The training scores utilized
+are highlighted in the table below.
+
+| Training Method | # Score |
+|:-------------:|:-----:|
+| Precision       | 1.0   |           
+| Recall         | 1.0   |            
+| F1-Score        | 1.0  |            
+| Accuracy       | 1.0   |          
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 24
+- eval_batch_size: 24
+- lr_scheduler_type: linear
+- num_epochs: 3
+- weight_decay: 0.01
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 0.2775        | 1.0   | 715  | 0.1784          | 0.8323 |
+| 0.146         | 2.0   | 1430 | 0.1624          | 0.8461 |
+| 0.0926        | 3.0   | 2145 | 0.1646          | 0.8587 |
+
+
+### Framework versions
+
+- Transformers 4.56.1
+- Pytorch 2.8.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.22.0