Update README.md

README.md

@@ -7,6 +7,13 @@ tags:
 model-index:
 - name: fine_tune_bert_output
   results: []
+language:
+- es
+metrics:
+- precision
+- recall
+- f1
+pipeline_tag: token-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -14,7 +21,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # fine_tune_bert_output
 
-This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
+This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an [wikiann](https://huggingface.co/datasets/unimelb-nlp/wikiann) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.3320
 - Overall Precision: 0.9051
@@ -25,17 +32,31 @@ It achieves the following results on the evaluation set:
 - Org F1: 0.8663
 - Per F1: 0.9367
 
-## Model description
+## Model Overview
+This is a BERT-based model fine-tuned on the CoNLL-2003 dataset for Named Entity Recognition (NER). It recognizes the following entities:
+- PER (Person)
+- LOC (Location)
+- ORG (Organization)
 
-More information needed
+## Intended Use
+- Text analysis for structured information extraction.
+- Applications like document parsing, chatbots, or customer service systems.
 
-## Intended uses & limitations
+### Inference Example
 
-More information needed
+```python
+from transformers import pipeline
 
-## Training and evaluation data
+# Load the model
+ner_pipeline = pipeline("ner", model="syubraj/espanyol_bert_based_ner")
 
-More information needed
+# Example text
+text = "Donald trabaja en Twitter"
+
+# Perform inference
+entities = ner_pipeline(text)
+print(entities)
+```
 
 ## Training procedure
 
@@ -73,4 +94,4 @@ The following hyperparameters were used during training:
 - Transformers 4.44.2
 - Pytorch 2.4.1+cu121
 - Datasets 3.2.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1