Update README.md

README.md

@@ -1,106 +1,119 @@
-# Fine-Tuned BERT Model for Named Entity Recognition (NER) with Accelerate Library
-
-This repository contains a fine-tuned BERT model for Named Entity Recognition (NER) tasks, trained on the [CoNLL 2003 dataset](https://huggingface.co/datasets/eriktks/conll2003) using the Hugging Face Accelerate library.
-
-The dataset includes the following labels:
-- `O`, `B-PER`, `I-PER`, `B-ORG`, `I-ORG`, `B-LOC`, `I-LOC`, `B-MISC`, `I-MISC`
-
-## Model Training Details
-
-### Training Arguments
-- **Library**: Hugging Face Accelerate
-- **Model Architecture**: `bert-base-cased` for token classification
-- **Learning Rate**: `2e-5`
-- **Number of Epochs**: `20`
-- **Weight Decay**: `0.01`
-- **Batch Size**: `8`
-- **Evaluation Strategy**: `epoch`
-- **Save Strategy**: `epoch`
-
-*Additional default parameters from the Accelerate and Transformers libraries were used.*
-
----
-
-## Evaluation Results
-
-### Validation Set Performance
-- **Overall Metrics**:
-  - Precision: 95.17%
-  - Recall: 93.87%
-  - F1 Score: 94.52%
-  - Accuracy: 98.62%
-
-#### Per-Label Performance
-| Entity Type | Precision | Recall | F1 Score |
-|-------------|-----------|--------|----------|
-| LOC         | 96.46%    | 96.51% | 96.49%   |
-| MISC        | 90.78%    | 89.14% | 89.95%   |
-| ORG         | 92.61%    | 90.26% | 91.42%   |
-| PER         | 97.94%    | 96.32% | 97.12%   |
-
-### Test Set Performance
-- **Overall Metrics**:
-  - Precision: 91.82%
-  - Recall: 89.68%
-  - F1 Score: 90.74%
-  - Accuracy: 97.23%
-
-#### Per-Label Performance
-| Entity Type | Precision | Recall | F1 Score |
-|-------------|-----------|--------|----------|
-| LOC         | 92.99%    | 92.10% | 92.54%   |
-| MISC        | 82.05%    | 75.00% | 78.37%   |
-| ORG         | 90.67%    | 88.28% | 89.46%   |
-| PER         | 96.04%    | 95.57% | 95.81%   |
-
----
-
-## How to Use the Model
-
-You can load the model directly from the Hugging Face Model Hub:
-
-```python
-from transformers import pipeline
-
-# Replace with your specific model checkpoint
-model_checkpoint = "Prikshit7766/bert-finetuned-ner-accelerate"
-token_classifier = pipeline(
-    "token-classification", 
-    model=model_checkpoint, 
-    aggregation_strategy="simple"
-)
-
-# Example usage
-result = token_classifier("My name is Sylvain and I work at Hugging Face in Brooklyn.")
-print(result)
-```
-
-### Example Output
-```python
-[
-   {
-      "entity_group": "PER",
-      "score": 0.9999658,
-      "word": "Sylvain",
-      "start": 11,
-      "end": 18
-   },
-   {
-      "entity_group": "ORG",
-      "score": 0.99996203,
-      "word": "Hugging Face",
-      "start": 33,
-      "end": 45
-   },
-   {
-      "entity_group": "LOC",
-      "score": 0.9999542,
-      "word": "Brooklyn",
-      "start": 49,
-      "end": 57
-   }
-]
-```
-
----
-
+---
+datasets:
+- eriktks/conll2003
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: token-classification
+---
+# Fine-Tuned BERT Model for Named Entity Recognition (NER) with Accelerate Library
+
+This repository contains a fine-tuned BERT model for Named Entity Recognition (NER) tasks, trained on the [CoNLL 2003 dataset](https://huggingface.co/datasets/eriktks/conll2003) using the Hugging Face Accelerate library.
+
+The dataset includes the following labels:
+- `O`, `B-PER`, `I-PER`, `B-ORG`, `I-ORG`, `B-LOC`, `I-LOC`, `B-MISC`, `I-MISC`
+
+## Model Training Details
+
+### Training Arguments
+- **Library**: Hugging Face Accelerate
+- **Model Architecture**: `bert-base-cased` for token classification
+- **Learning Rate**: `2e-5`
+- **Number of Epochs**: `20`
+- **Weight Decay**: `0.01`
+- **Batch Size**: `8`
+- **Evaluation Strategy**: `epoch`
+- **Save Strategy**: `epoch`
+
+*Additional default parameters from the Accelerate and Transformers libraries were used.*
+
+---
+
+## Evaluation Results
+
+### Validation Set Performance
+- **Overall Metrics**:
+  - Precision: 95.17%
+  - Recall: 93.87%
+  - F1 Score: 94.52%
+  - Accuracy: 98.62%
+
+#### Per-Label Performance
+| Entity Type | Precision | Recall | F1 Score |
+|-------------|-----------|--------|----------|
+| LOC         | 96.46%    | 96.51% | 96.49%   |
+| MISC        | 90.78%    | 89.14% | 89.95%   |
+| ORG         | 92.61%    | 90.26% | 91.42%   |
+| PER         | 97.94%    | 96.32% | 97.12%   |
+
+### Test Set Performance
+- **Overall Metrics**:
+  - Precision: 91.82%
+  - Recall: 89.68%
+  - F1 Score: 90.74%
+  - Accuracy: 97.23%
+
+#### Per-Label Performance
+| Entity Type | Precision | Recall | F1 Score |
+|-------------|-----------|--------|----------|
+| LOC         | 92.99%    | 92.10% | 92.54%   |
+| MISC        | 82.05%    | 75.00% | 78.37%   |
+| ORG         | 90.67%    | 88.28% | 89.46%   |
+| PER         | 96.04%    | 95.57% | 95.81%   |
+
+---
+
+## How to Use the Model
+
+You can load the model directly from the Hugging Face Model Hub:
+
+```python
+from transformers import pipeline
+
+# Replace with your specific model checkpoint
+model_checkpoint = "Prikshit7766/bert-finetuned-ner-accelerate"
+token_classifier = pipeline(
+    "token-classification", 
+    model=model_checkpoint, 
+    aggregation_strategy="simple"
+)
+
+# Example usage
+result = token_classifier("My name is Sylvain and I work at Hugging Face in Brooklyn.")
+print(result)
+```
+
+### Example Output
+```python
+[
+   {
+      "entity_group": "PER",
+      "score": 0.9999658,
+      "word": "Sylvain",
+      "start": 11,
+      "end": 18
+   },
+   {
+      "entity_group": "ORG",
+      "score": 0.99996203,
+      "word": "Hugging Face",
+      "start": 33,
+      "end": 45
+   },
+   {
+      "entity_group": "LOC",
+      "score": 0.9999542,
+      "word": "Brooklyn",
+      "start": 49,
+      "end": 57
+   }
+]
+```
+
+---