Update README.md

README.md

@@ -39,9 +39,62 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Please proceed the following example **that purely relies on tranformers and torch** on google colab or here:
+
+1. Setup ask method for inferring FlanT5 as follows: 
+```python
+def ask(prompt):
+  inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
+  inputs.to(device)
+  output = model.generate(**inputs, max_length=320, temperature=1)
+  return tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+```
+
+2. Setup chain and expected output labels:
+```python
+def emotion_extraction_chain(context, target):
+  # Setup labels.
+  labels_list = ["anger", "disgust", "fear", "joy", "sadness", "surprise", "neutral"]
+  # Setup Chain-of-Thought
+  step1 = f"Given the conversation {context}, which text spans are possibly causes emotion on {target}?"
+  span = ask(step1)
+  step2 = f"{step1}. The mentioned text spans are about {span}. Based on the common sense, what " + f"is the implicit opinion towards the mentioned text spans that causes emotion on {target}, and why?"
+  opinion = ask(step2)
+  step3 = f"{step2}. The opinion towards the text spans that causes emotion on {target} is {opinion}. " + f"Based on such opinion, what is the emotion state of {target}?" 
+  emotion_state = ask(step3)
+  step4 = f"{step3}. The emotion state is {emotion_state}. Based on these contexts, summarize and return the emotion cause only." + "Choose from: {}.".format(", ".join(labels_list))
+  # Return the final response.
+  return ask(step4)
+```
+
+3. Initialize `device`, `model` and `tokenizer` as follows:
+```python
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+
+model_path = "nicolay-r/flan-t5-emotion-cause-thor-base"
+device = "cuda:0"
+
+model = T5ForConditionalGeneration.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model.to(device)
+```
+
+4. Apply it!
+```python
+# setup history context (conv_turn_1)
+conv_turn_1 = "John: ohh you made up!"
+# setup utterance.
+conv_turn_2 = "Jake: yaeh, I could not be mad at him for too long!"
+context = conv_turn_1 + conv_turn_2
+# Target is considered as the whole conv-turn mentioned in context.
+target = conv_turn_2
+flant5_response = emotion_extraction_chain(context, target)
+print(f"Emotion state of the speaker of `{target}` is: {flant5_response}")
+```
+
+The response is as follows:
+>>> Emotion state of the speaker of `Jake: yaeh, I could not be mad at him for too long!` is: **anger**
 
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 ### Downstream Use [optional]
 
@@ -128,74 +181,5 @@ Use the code below to get started with the model.
 
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-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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