Update README.md

README.md

@@ -24,6 +24,24 @@ It achieves the following results on the evaluation set:
 
 More information needed
 
+## How to use
+
+You can use this model directly with a pipeline for text classification:
+
+```python
+>>> from transformers import pipeline
+>>> import torch
+>>> bert_ckpt = "transformersbook/bert-base-uncased-finetuned-clinc"
+>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+>>> pipe = pipeline("text-classification", model=bert_ckpt, device=device)
+
+
+>>> query = """Hey, I'd like to rent a vehicle from Nov 1st to Nov 15th in Paris and I need a 15 passenger van"""
+>>> print(pipe(query))
+
+[{'label': 'car_rental', 'score': 0.5490034222602844}]
+```
+
 ## Intended uses & limitations
 
 More information needed
@@ -62,56 +80,3 @@ 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)
-```