---
license: apache-2.0
tags:
- generated_from_trainer
widget:
- text: Toward Annotator Group Bias in Crowdsourcing. Introduction
  example_title: Introduction
- text: Over the last few years, there has been a move towards data
  example_title: Over the last few years
- text: We introduce a new language representation
  example_title: new language representation
- text: Acknowledgements. This research is supported by the National Science Foundation
  example_title: Acknowledgements
- text: 'We hope that our work serves not only to inform the NLP '
  example_title: We hope that
base_model: distilgpt2
model-index:
- name: distilgpt2-finetune-acl22
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilgpt2-finetune-acl22

This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the [ACL-anthology-corpus](https://github.com/shauryr/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.