Update README.md

README.md

@@ -2,11 +2,18 @@
 license: apache-2.0
 tags:
 - generated_from_trainer
+- finance
+- intent-classification
 datasets:
 - banking77
 model-index:
 - name: banking-intent-distilbert-classifier
   results: []
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -24,19 +31,40 @@ It achieves the following results on the evaluation set:
 - epoch: 10.0
 - step: 3130
 
-## Model description
+_Note: This is just a simple example of fine-tuning a DistilBERT model for 
+multi-class classification task to see how much it costs to train this 
+model on Google Cloud (using a T4 GPU). It costs me about 1.07 SGD and 
+takes less than 20 mins to complete the training. Although my intention was just 
+to test it out on Google Cloud, the model has been appropriately trained 
+and is now ready to be used. Hopefully, it is what you're looking for._
 
-More information needed
+## Inference example
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-## Intended uses & limitations
+tokenizer = AutoTokenizer.from_pretrained("lxyuan/banking-intent-distilbert-classifier")
+model = AutoModelForSequenceClassification.from_pretrained("lxyuan/banking-intent-distilbert-classifier")
 
-More information needed
+banking_intend_classifier = TextClassificationPipeline(
+  model=model,
+  tokenizer=tokenizer,
+  device=0
+)
+
+banking_intend_classifier("How to report lost card?")
+>>> [{'label': 'lost_or_stolen_card', 'score': 0.9518502950668335}]
+
+```
 
 ## Training and evaluation data
 
-More information needed
+The BANKING77 dataset consists of online banking queries labeled with their corresponding intents, 
+offering a comprehensive collection of 77 finely categorized intents within the banking domain. 
+With a total of 13,083 customer service queries, it specifically emphasizes precise intent detection
+within a single domain.
 
 ## Training procedure
+To reproduce the result, please refer to this [notebook](https://github.com/LxYuan0420/nlp/blob/main/notebooks/distillbert-intent-classification-banking.ipynb)
 
 ### Training hyperparameters
 
@@ -57,4 +85,4 @@ The following hyperparameters were used during training:
 - Transformers 4.29.2
 - Pytorch 1.9.0+cu111
 - Datasets 2.12.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3