Update README.md

README.md

@@ -1,8 +1,6 @@
 ---
 license: mit
 base_model: xlm-roberta-base
-tags:
-- generated_from_trainer
 datasets:
 - xtreme
 metrics:
@@ -35,6 +33,8 @@ model-index:
     - name: Accuracy
       type: accuracy
       value: 0.9194332683336213
+language:
+- en
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -50,19 +50,25 @@ It achieves the following results on the evaluation set:
 - F1: 0.8057
 - Accuracy: 0.9194
 
-## Model description
+## Intended uses & limitations
 
-More information needed
+#### How to use
 
-## Intended uses & limitations
+You can use this model with Transformers *pipeline* for NER.
 
-More information needed
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
 
-## Training and evaluation data
+tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
+model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
 
-More information needed
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "My name is Wolfgang and I live in Berlin"
 
-## Training procedure
+ner_results = nlp(example)
+print(ner_results)
+```
 
 ### Training hyperparameters
 
@@ -91,4 +97,4 @@ The following hyperparameters were used during training:
 - Transformers 4.33.0
 - Pytorch 2.0.0
 - Datasets 2.1.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3