distilgpt2-finetune-acl22

This model is a fine-tuned version of distilgpt2 on the ACL-anthology-corpus dataset. It achieves the following results on the evaluation set:

• Loss: 3.4835

Model description

We finetune the gpt2 LLM on the full-text from ACL-anthology-corpus

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 256
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
3.6676	1.0	9852	3.5623
3.5959	2.0	19704	3.4995
3.5719	3.0	29556	3.4835

Framework versions

• Transformers 4.21.2
• Pytorch 1.12.1
• Datasets 2.4.0
• Tokenizers 0.12.1

What can it do?

Write introductions/abstract

• Prompt : Toward Annotator Group Bias in Crowdsourcing. Introduction
• Generation : Toward Annotator Group Bias in Crowdsourcing. Introduction Online platforms for crowdsourcing have received increasing scrutiny in recent years as platforms for online data analytics require an additional layer of content that allows users to interact and be informed about their quality.