Welcome to the world of AI-powered feedback generation! In this post, we will explore how to use a finely-tuned model for providing short answer feedback specifically designed for micro-job scenarios. This model is a compact version of the powerful mBART architecture, enhanced to generate feedback based on user inputs.

Understanding the Model

Before diving into code usage, think of the model as a smart assistant in a classroom. Just as a teacher evaluates student answers and offers feedback, our model reviews the inputs—your responses—and generates feedback in terms of correctness and guidance based on a reference answer.

Installation and Setup

• Ensure you have the latest version of Transformers, PyTorch, and Datasets.
• Install the necessary libraries via pip:

  pip install transformers torch datasets

Code Implementation

Now, let’s see how to apply the model to generate feedback:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("Short-Answer-Feedback/mbart-finetuned-saf-micro-job")
tokenizer = AutoTokenizer.from_pretrained("Short-Answer-Feedback/mbart-finetuned-saf-micro-job")

# Define example input
example_input = "Antwort: Ich gebe mich zu erkennen und zeige das Informationsschreiben vor. Lösung: Der Jobber soll sich in diesem Fall dem Personal gegenüber zu erkennen geben (0.25 P) und das entsprechende Informationsschreiben in der App vorzeigen (0.25 P). Frage: Frage 1: Wie reagierst du, wenn du auf deine Tätigkeit angesprochen wirst?"

# Tokenize and generate feedback
inputs = tokenizer(example_input, max_length=256, padding="max_length", truncation=True, return_tensors="pt")
generated_tokens = model.generate(inputs["input_ids"], attention_mask=inputs["attention_mask"], max_length=128)
output = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]

print(output)

The lines of code above do the following:

They import the necessary libraries, similar to gathering supplies before starting a school project.

You load your model and tokenizer—like setting up your classroom.

The input is prepared which includes a response and the expected feedback.

Finally, feedback is generated and printed out, akin to receiving a teacher’s evaluation.

Troubleshooting Ideas

While using this model, you may encounter some hiccups here and there. Here’s how to resolve them:

• Issue: Model doesn’t provide useful feedback.
  • Make sure the question and answers align with the training data.
  • Expanding the dataset with more context-specific examples might improve the model’s performance.
• Issue: Slow performance.
  • Check to see if you are using a GPU for faster computation—like having a turbo engine in a race car.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

This short guide walks you through the essentials of using our micro job feedback model. Treat it as your assistant in the feedback process, helping to ensure that every answer gets the evaluation it deserves.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.