Update README.md

README.md

@@ -7,12 +7,18 @@ tags: []
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-This project involves fine-tuning a BERT-based model (dslim/bert-large-NER) to perform Named Entity Recognition (NER) on mountain names in text. The model has been trained to identify mentions of mountain names and differentiate them from other geographic entities or non-entities.
+This project involves fine-tuning a BERT-based model (dslim/bert-large-NER) to perform Named Entity Recognition (NER) on mountain names in text. 
+
+The model has been trained to identify mentions of mountain names and differentiate them from other geographic entities or non-entities.
 
 Features:
+
 Fine-tuned on a custom dataset that includes sentences both with and without mountain names.
+
 Uses focal loss to handle class imbalance, which ensures the model focuses on correctly classifying rare mountain names.
+
 Token-level classification for identifying the B-MOUNTAIN, I-MOUNTAIN, and O (non-entity) labels.
+
 Balances training between sentences with mountains (80%) and without mountains (20%).
 
 
@@ -20,7 +26,7 @@ Balances training between sentences with mountains (80%) and without mountains (
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. 
 
 - **Developed by:** Oleksandr Kharytonov
 - **Model type:** BERT
@@ -38,7 +44,7 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-```
+```python
 from transformers import AutoTokenizer, AutoModelForTokenClassification
 
 tokenizer = AutoTokenizer.from_pretrained('./saved_model')
@@ -50,13 +56,13 @@ model = AutoModelForTokenClassification.from_pretrained('./saved_model')
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the github below to get started with the model.
 
-[More Information Needed]
+https://github.com/Shah1st/mountain-ner
 
 ## Training Details
 
@@ -74,8 +80,7 @@ Filter for sentences with 'fine_ner_tags' == 24 (mountains)
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
-'eval_loss': 0.009154710918664932, "eval_precision_'O'": 0.9846412638045784, "eval_recall_'O'": 0.9890537760799295, "eval_f1_'O'": 0.9868425875022907,
-'eval_macro_f1': 0.8952192988290304, 'eval_accuracy': 0.9746226793108054
+'eval_loss': 0.009154710918664932, 'eval_macro_f1': 0.8952192988290304, 'eval_accuracy': 0.9746226793108054
 
 ### Testing Data, Factors & Metrics