gpt2-finetuned-mcqa – AI Model by mNLP-project | AlphaNeural AI

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mNLP-project/
gpt2-finetuned-mcqa

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text-generation

generated_from_trainer

openai-community/gpt2

autotrain_compatible

text-generation-inference

endpoints_compatible

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gpt2-finetuned-mcqa

This model is a fine-tuned version of openai-community/gpt2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.4696
Bertscore Precision: 0.1390
Bertscore Recall: 0.1471
Bertscore F1: 0.1428

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Bertscore Precision	Bertscore Recall	Bertscore F1
4.6412	1.0000	11719	2.9435	0.1223	0.1435	0.1316
4.4659	2.0	23439	2.8198	0.1268	0.1445	0.1346
4.1443	3.0000	35158	2.7298	0.1326	0.1452	0.1384
4.1037	4.0	46878	2.6608	0.1348	0.1457	0.1399
4.0107	5.0000	58597	2.5980	0.1338	0.1462	0.1395
4.0216	6.0	70317	2.5494	0.1353	0.1463	0.1404
3.5677	7.0000	82036	2.5156	0.1371	0.1466	0.1415
3.619	8.0	93756	2.4859	0.1376	0.1468	0.1419
3.5389	9.0000	105475	2.4735	0.1391	0.1470	0.1429
3.713	9.9996	117190	2.4696	0.1390	0.1471	0.1428

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1