Update README.md

README.md

@@ -29,8 +29,70 @@ pipeline_tag: text2text-generation
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
+This sequence of scripts represent a purely `torch` and `transformers` based model usage for inference.
+
+This example is also available on [GoogleColab](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/FlanT5_Finetuned_Model_Usage.ipynb)
+
+Here are the **following three steps for a quick start with model application**:
+
+
+1. Loading model and tokenizer
+
+```python
+import torch
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+
+# Setup model path.
+model_path = "nicolay-r/flan-t5-tsa-thor-xl"
+# Setup device.
+device = "cuda:0"
+
+model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model.to(device)
+```
+
+2. Setup ask method for generating LLM responses
+```python
+def ask(prompt):
+  inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
+  inputs.to(device)
+  output = model.generate(**inputs, temperature=1)
+  return tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+```
+
+2. Setup Chain-of-Thought
+```python
+def target_sentiment_extraction(sentence, target):
+  # Setup labels.
+  labels_list = ['neutral', 'positive', 'negative']
+  # Setup Chain-of-Thought
+  step1 = f"Given the sentence {sentence}, which specific aspect of {target} is possibly mentioned?"
+  aspect = ask(step1)
+  step2 = f"{step1}. The mentioned aspect is about {aspect}. Based on the common sense, what is the implicit opinion towards the mentioned aspect of {target}, and why?"
+  opinion = ask(step2)
+  step3 = f"{step2}. The opinion towards the mentioned aspect of {target} is {opinion}. Based on such opinion, what is the sentiment polarity towards {target}?"
+  emotion_state = ask(step3)
+  step4 = f"{step3}. The sentiment polarity is {emotion_state}. Based on these contexts, summarize and return the sentiment polarity only, " + "such as: {}.".format(", ".join(labels_list))
+  # Return the final response.
+  return ask(step4)
+```
+
+Finally, you can infer model results as follows:
+```python
+# Input sentence.
+sentence = "I would support him despite his bad behavior."
+# Input target.
+target = "him"
+# output response
+flant5_response = target_sentiment_extraction(sentence, target)
+print(f"Author opinion towards `{target}` in `{sentence}` is:\n{flant5_response}")
+```
+
+The response of the model is as follows:
+> Author opinion towards `him` in `I would support him despite his bad behavior.` is: **positive**
+ 
 ### Downstream Use
 
 Please refer to the [related section](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework?tab=readme-ov-file#three-hop-chain-of-thought-thor) of the **Reasoning-for-Sentiment-Analysis** Framework