Update README.md

README.md

@@ -42,27 +42,51 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-finetuned-ner
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0599
-- Precision: 0.9347
-- Recall: 0.9512
-- F1: 0.9429
-- Accuracy: 0.9864
+## Model Description
+This model is a Named Entity Recognition (NER) model built using PyTorch and trained on the CoNLL-2003 dataset. The model is designed to identify and classify named entities in text into categories such as persons (PER), organizations (ORG), locations (LOC), and miscellaneous entities (MISC).
 
-## Model description
+## Intended Uses & Limitations
+**Intended Uses:**
+- **Text Analysis:** This model can be used for extracting named entities from unstructured text data, which is useful in various NLP tasks such as information retrieval, content categorization, and automated summarization.
+- **NER Task:** Specifically designed for NER tasks in English.
 
-More information needed
+**Limitations:**
+- **Language Dependency:** The model is trained on English data and may not perform well on texts in other languages.
+- **Domain Specificity:** Performance may degrade on text from domains significantly different from the training data.
+- **Error Propagation:** Incorrect predictions may propagate to downstream tasks, affecting overall performance.
 
-## Intended uses & limitations
+## How to Use
+To use this model, load it through the Hugging Face Transformers library. Below is a basic example:
 
-More information needed
+```python
+from transformers import pipeline
 
-## Training and evaluation data
+# Load the NER pipeline
+ner_pipeline = pipeline("ner", model="Ashaduzzaman/bert-finetuned-ner")
 
-More information needed
+# Example text
+text = "Hugging Face Inc. is based in New York City."
 
-## Training procedure
+# Perform NER
+entities = ner_pipeline(text)
+
+print(entities)
+```
+
+## Limitations and Bias
+- **Bias in Data:** The model is trained on the CoNLL-2003 dataset, which may contain biases related to the sources of the text. The model might underperform on entities not well represented in the training data.
+- **Overfitting:** The model may overfit to the specific entities present in the CoNLL-2003 dataset, affecting its generalization to new entities or text styles.
+
+## Training Data
+The model was trained on the CoNLL-2003 dataset, a widely used benchmark dataset for NER tasks. The dataset contains annotated text from news articles, with labels for persons, organizations, locations, and miscellaneous entities.
+
+## Training Procedure
+The model was fine-tuned using a pre-trained transformer model (e.g., BERT) with a token classification head for NER. The training involved:
+- **Optimizer:** AdamW optimizer
+- **Learning Rate:** Learning rate scheduler was employed
+- **Batch Size:** Defined in the notebook based on available resources
+- **Epochs:** The model was trained for a specified number of epochs until convergence
+- **Evaluation:** Model performance was evaluated on a validation set, with metrics like F1-score, precision, and recall.
 
 ### Training hyperparameters
 
@@ -75,7 +99,8 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 3
 
-### Training results
+## Evaluation Results
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the CoNLL-2003 test set, with performance measured using standard NER metrics:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
@@ -83,8 +108,9 @@ The following hyperparameters were used during training:
 | 0.0359        | 2.0   | 3512 | 0.0693          | 0.9265    | 0.9418 | 0.9341 | 0.9847   |
 | 0.0222        | 3.0   | 5268 | 0.0599          | 0.9347    | 0.9512 | 0.9429 | 0.9864   |
 
+These results indicate the model's ability to correctly identify and classify named entities in text.
 
-### Framework versions
+## Framework versions
 
 - Transformers 4.42.4
 - Pytorch 2.3.1+cu121