Update README.md

README.md

@@ -9,6 +9,7 @@ This model is a fine-tuned version of XLM-RoBERTa (xlm-roberta-base) for Named E
 
 PER: Person names
 ORG: Organization names
+LOC: Location names
 
 
 <!-- Provide a quick summary of what the model is/does. -->
@@ -40,7 +41,12 @@ This modelcard aims to be a base template for new models. It has been generated
 - **Demo [optional]:** [More Information Needed]
 
 ## Uses
+This model is suitable for multilingual NER tasks, especially in scenarios where extracting and classifying person, organization, and location names in text across different languages is required.
 
+Applications:
+Information extraction
+Multilingual NER tasks
+Automated text analysis for businesses
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 ### Direct Use
@@ -80,6 +86,10 @@ Use the code below to get started with the model.
 [More Information Needed]
 
 ## Training Details
+Base Model: xlm-roberta-base
+Training Dataset: The model is trained on the PAN-X subset of the XTREME dataset, which includes labeled NER data for multiple languages.
+Training Framework: Hugging Face transformers library with PyTorch backend.
+Data Preprocessing: Tokenization was performed using XLM-RoBERTa tokenizer, with attention paid to aligning token labels to subword tokens.
 
 ### Training Data
 
@@ -97,6 +107,7 @@ Use the code below to get started with the model.
 
 
 #### Training Hyperparameters
+The model's performance is evaluated using the F1 score for NER. The predictions are aligned with gold-standard labels, ignoring sub-token predictions where appropriate.
 
 - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->