Update README.md

README.md

@@ -7,6 +7,8 @@ datasets:
 - universal_dependencies
 metrics:
 - accuracy
+- precision
+- recall
 model-index:
 - name: mdeberta-v3-ud-thai-pud-upos
   results:
@@ -23,6 +25,9 @@ model-index:
     - name: Accuracy
       type: accuracy
       value: 0.9934846474601972
+language:
+- th
+library_name: transformers
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,17 +48,21 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model is train on thai UD Thai PUD corpus with `Universal Part-of-speech (UPOS)` tag to help with pos tagging in Thai language.
 
-## Intended uses & limitations
+## Example
+```python
+from transformers import AutoModelForTokenClassification, AutoTokenizer, TokenClassificationPipeline
 
-More information needed
+model = AutoModelForTokenClassification.from_pretrained("Pavarissy/mdeberta-v3-ud-thai-pud-upos")
+tokenizer = AutoTokenizer.from_pretrained("Pavarissy/mdeberta-v3-ud-thai-pud-upos")
 
-## Training and evaluation data
+pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer, grouped_entities=True)
+outputs = pipeline("ประเทศไทย อยู่ใน ทวีป เอเชีย")
+print(outputs)
+# [{'entity_group': 'PROPN', 'score': 0.9946701, 'word': 'ประเทศไทย', 'start': 0, 'end': 9}, {'entity_group': 'VERB', 'score': 0.85809743, 'word': 'อยู่ใน', 'start': 9, 'end': 16}, {'entity_group': 'NOUN', 'score': 0.99632, 'word': 'ทวีป', 'start': 16, 'end': 21}, {'entity_group': 'PROPN', 'score': 0.9961184, 'word': 'เอเชีย', 'start': 21, 'end': 28}]
 
-More information needed
-
-## Training procedure
+```
 
 ### Training hyperparameters
 
@@ -87,4 +96,4 @@ The following hyperparameters were used during training:
 - Transformers 4.34.1
 - Pytorch 2.1.0+cu118
 - Datasets 2.14.6
-- Tokenizers 0.14.1
+- Tokenizers 0.14.1