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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.
