take a codegen model from huggingface and run evaluation on it in python

To run evaluation on a pre-trained codegen model from HuggingFace, you'll need to follow these steps:

1. Install the Transformers library from HuggingFace by running pip install transformers in your terminal.
2. Load the pre-trained codegen model using the AutoModelForSeq2SeqLM class from the Transformers library. You can specify the model name or model path as an argument. For example, to load the GPT-2 model with 117M parameters, you can use the following code:

main.py
from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("gpt2")
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3. Prepare your evaluation data. This will depend on your specific task and dataset. Generally, you will need to tokenize your input data and create a dataloader.
4. Evaluate the model on your data using the generate method of the model. This will generate predicted outputs based on your input data. You can then compare the predicted outputs to the ground truth outputs to calculate metrics such as accuracy, F1 score, or BLEU score.

Here is an example of how to use the generate method with the GPT-2 model to generate text:

main.py
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("gpt2")

input_text = "Hello, how are you today?"

input_ids = tokenizer.encode(input_text, return_tensors='pt')

generated_output = model.generate(input_ids)

output_text = tokenizer.decode(generated_output[0], skip_special_tokens=True)
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You can then evaluate the output_text against the ground truth to calculate your evaluation metrics.