Update README.md

README.md

@@ -13,6 +13,64 @@ probably proofread and complete it, then remove this comment. -->
 
 This model is [t5-base](https://huggingface.co/t5-base) fine-tuned on the [190k Medium Articles](https://www.kaggle.com/datasets/fabiochiusano/medium-articles) dataset for predicting article titles using the article textual content as input.
 
+# How to use the model
+
+```
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import nltk
+nltk.download('punkt')
+
+tokenizer = AutoTokenizer.from_pretrained("fabiochiu/t5-small-medium-title-generation")
+model = AutoModelForSeq2SeqLM.from_pretrained("fabiochiu/t5-small-medium-title-generation")
+
+text = """
+Many financial institutions started building conversational AI, prior to the Covid19
+pandemic, as part of a digital transformation initiative. These initial solutions
+were high profile, highly personalized virtual assistants — like the Erica chatbot
+from Bank of America. As the pandemic hit, the need changed as contact centers were
+under increased pressures. As Cathal McGloin of ServisBOT explains in “how it started,
+and how it is going,” financial institutions were looking for ways to automate
+solutions to help get back to “normal” levels of customer service. This resulted
+in a change from the “future of conversational AI” to a real tactical assistant
+that can help in customer service. Haritha Dev of Wells Fargo, saw a similar trend.
+Banks were originally looking to conversational AI as part of digital transformation
+to keep up with the times. However, with the pandemic, it has been more about
+customer retention and customer satisfaction. In addition, new use cases came about
+as a result of Covid-19 that accelerated adoption of conversational AI. As Vinita
+Kumar of Deloitte points out, banks were dealing with an influx of calls about new
+concerns, like questions around the Paycheck Protection Program (PPP) loans. This
+resulted in an increase in volume, without enough agents to assist customers, and
+tipped the scale to incorporate conversational AI. When choosing initial use cases
+to support, financial institutions often start with high volume, low complexity
+tasks. For example, password resets, checking account balances, or checking the
+status of a transaction, as Vinita points out. From there, the use cases can evolve
+as the banks get more mature in developing conversational AI, and as the customers
+become more engaged with the solutions. Cathal indicates another good way for banks
+to start is looking at use cases that are a pain point, and also do not require a
+lot of IT support. Some financial institutions may have a multi-year technology
+roadmap, which can make it harder to get a new service started. A simple chatbot
+for document collection in an onboarding process can result in high engagement,
+and a high return on investment. For example, Cathal has a banking customer that
+implemented a chatbot to capture a driver’s license to be used in the verification
+process of adding an additional user to an account — it has over 85% engagement
+with high satisfaction. An interesting use case Haritha discovered involved
+educating customers on financial matters. People feel more comfortable asking a
+chatbot what might be considered a “dumb” question, as the chatbot is less judgmental.
+Users can be more ambiguous with their questions as well, not knowing the right
+words to use, as chatbot can help narrow things down.
+"""
+
+inputs = ["summarize: " + text]
+
+inputs = tokenizer(inputs, max_length=max_input_length, truncation=True, return_tensors="pt")
+output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64)
+decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+predicted_title = nltk.sent_tokenize(decoded_output.strip())[0]
+
+print(predicted_title)
+# Conversational AI: The Future of Customer Service
+```
+
 ## Training and evaluation data
 
 The model has been trained on a single epoch spanning about 16000 articles, evaluating on 1000 random articles not used during training.