Update README.md

README.md

@@ -38,55 +38,95 @@ model-index:
       value: 0.9782491655001615
 ---
 
-<!-- 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. -->
 
-# base-NER
+# base-NER: A Named Entity Recognition (NER) Model
 
-This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the conll2003 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1129
-- Precision: 0.8845
-- Recall: 0.9017
-- F1: 0.8930
-- Accuracy: 0.9782
+`base-NER` is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the CoNLL2003 dataset, designed for the task of **Named Entity Recognition (NER)**. This model can identify entities like people, organizations, locations, and more from text. 
 
-## Model description
 
-More information needed
+```python
+from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
 
-## Intended uses & limitations
+model = AutoModelForTokenClassification.from_pretrained("eddiegulay/base-NER")
+tokenizer = AutoTokenizer.from_pretrained("eddiegulay/base-NER")
 
-More information needed
+classifier = pipeline("ner", model=model, tokenizer=tokenizer)
+result = classifier("My name is Edgar and I stay in Dar es Salaam")
+print(result)
+```
 
-## Training and evaluation data
 
-More information needed
+## Model Performance
 
-## Training procedure
+The model achieved the following results on the CoNLL2003 test set:
+- **Precision**: 0.8845
+- **Recall**: 0.9017
+- **F1-Score**: 0.8930
+- **Accuracy**: 0.9782
 
-### Training hyperparameters
+The loss during training was 0.1129 on the validation set.
+
+## Model Description
+
+This model leverages the DistilBERT architecture, which is a smaller and faster version of BERT, designed for efficiency while maintaining strong performance. The model is specifically fine-tuned for NER tasks, making it ideal for entity extraction in various domains like finance, healthcare, or general text analytics.
+
+## Intended Uses & Limitations
+
+**Intended Uses**:  
+- Text extraction tasks for recognizing names of people, organizations, locations, dates, and other named entities in a sentence.
+- Suitable for use in production applications where lightweight models are preferred due to memory or speed constraints.
+
+**Limitations**:  
+- The model is limited to English texts, as it was trained on the CoNLL2003 dataset.
+- Performance may degrade when used on domain-specific entities not present in the CoNLL2003 dataset (e.g., technical or biomedical domains).
+- May struggle with ambiguous or context-dependent entity classifications.
+
+## Training and Evaluation Data
+
+The model was trained on the **CoNLL2003** dataset, which contains annotations for named entities in English text. It is a widely-used dataset for NER tasks, consisting of four entity types: **person**, **organization**, **location**, and **miscellaneous**.
+
+### Dataset Configuration
+- **Dataset**: CoNLL2003
+- **Split**: Test set used for evaluation
+- **Entity Types**: Person, Organization, Location, Miscellaneous
+
+## Training Procedure
+
+The model was fine-tuned for 2 epochs using a linear learning rate scheduler and an Adam optimizer.
+
+### Training Hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 2
+- **Learning Rate**: 2e-5
+- **Batch Size**: 16 (train and eval)
+- **Seed**: 42
+- **Optimizer**: Adam (betas=(0.9,0.999), epsilon=1e-8)
+- **Scheduler**: Linear
+- **Epochs**: 2
 
-### Training results
+### Training Results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
 | 0.0595        | 1.0   | 878  | 0.1046          | 0.8676    | 0.8909 | 0.8791 | 0.9762   |
 | 0.0319        | 2.0   | 1756 | 0.1129          | 0.8845    | 0.9017 | 0.8930 | 0.9782   |
 
+## Usage Example
+
+You can use this model with Hugging Face's `transformers` library for token classification tasks:
 
-### Framework versions
+
+## Framework Versions
 
 - Transformers 4.44.2
 - Pytorch 2.4.0+cu121
 - Datasets 2.21.0
 - Tokenizers 0.19.1
+
+## Future Improvements
+
+- Fine-tuning the model on more domain-specific datasets for improved generalization.
+- Implementing entity recognition for additional entity types, including products, dates, and technical terms.
+  
+Feel free to modify or add more details, especially for sections like model description, intended uses, and limitations.
+