Update README.md

README.md

@@ -62,3 +62,56 @@ The following hyperparameters were used during training:
 - Pytorch 2.3.1+cu121
 - Datasets 2.20.0
 - Tokenizers 0.19.1
+
+### How to use
+
+You can use this model directly with a pipeline for masked language modeling:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='bert-base-cased')
+>>> unmasker("Hello I'm a [MASK] model.")
+
+[{'sequence': "[CLS] Hello I'm a fashion model. [SEP]",
+  'score': 0.09019174426794052,
+  'token': 4633,
+  'token_str': 'fashion'},
+ {'sequence': "[CLS] Hello I'm a new model. [SEP]",
+  'score': 0.06349995732307434,
+  'token': 1207,
+  'token_str': 'new'},
+ {'sequence': "[CLS] Hello I'm a male model. [SEP]",
+  'score': 0.06228214129805565,
+  'token': 2581,
+  'token_str': 'male'},
+ {'sequence': "[CLS] Hello I'm a professional model. [SEP]",
+  'score': 0.0441727414727211,
+  'token': 1848,
+  'token_str': 'professional'},
+ {'sequence': "[CLS] Hello I'm a super model. [SEP]",
+  'score': 0.03326151892542839,
+  'token': 7688,
+  'token_str': 'super'}]
+```
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+from transformers import BertTokenizer, BertModel
+tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+model = BertModel.from_pretrained("bert-base-cased")
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+and in TensorFlow:
+
+```python
+from transformers import BertTokenizer, TFBertModel
+tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+model = TFBertModel.from_pretrained("bert-base-cased")
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+```