gpt2-finetuned-mcqa-sciq2-safety – AI Model by mNLP-project | AlphaNeural AI

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gpt2-finetuned-mcqa-sciq2-safety

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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-sciq2-safety

This model is a fine-tuned version of openai-community/gpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.7824
Bleu: 0.0816
Bertscore Precision: 0.4316
Bertscore Recall: 0.4553
Bertscore F1: 0.4431

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	Bleu	Bertscore Precision	Bertscore Recall	Bertscore F1
1.917	1.0	1460	1.8258	0.0856	0.4279	0.4541	0.4406
1.7299	2.0	2920	1.7928	0.0789	0.4311	0.4549	0.4427
1.6342	3.0	4380	1.7870	0.0858	0.4315	0.4542	0.4425
1.5534	4.0	5840	1.7824	0.0816	0.4316	0.4553	0.4431
1.4878	5.0	7300	1.7909	0.0797	0.4315	0.4556	0.4432
1.4554	6.0	8760	1.8021	0.0772	0.4312	0.4557	0.4431
1.3981	7.0	10220	1.8103	0.0767	0.4314	0.4558	0.4433
1.3712	8.0	11680	1.8242	0.0427	0.4315	0.4561	0.4434
1.3431	9.0	13140	1.8338	0.0416	0.4315	0.4560	0.4434
1.3354	10.0	14600	1.8390	0.0420	0.4315	0.4560	0.4434

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1