This model has been trained on a diverse dataset consisting of over 68,493 lines of data, encompassing a wide range of Hindi names and surnames along with their English counterparts. Evaluation data has been carefully selected to ensure reliable and accurate translation performance.

Training Procedure

1 days of training

Hardware Environment:

Azure Studio
Standard_DS12_v2
4 cores, 28GB RAM, 56GB storage
Data manipulation and training on medium-sized datasets (1-10GB)
6 cores
Loss: 0.4618
Bleu: 70.7674
Gen Len: 10.2548

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
1.0715	1.0	1600	0.9902	41.5519	5.8328
0.8185	2.0	3200	0.9547	53.8222	5.7988
0.6909	3.0	4800	0.9527	54.7846	5.8169
0.6038	4.0	6400	0.9496	55.6009	5.8406

Framework versions

Transformers 4.39.1
Pytorch 2.2.2+cpu
Datasets 2.15.0
Tokenizers 0.15.2

ihebaker10/spark-name-de-to-en

German Names to English Translation Model

Model Overview

Intended Uses and Limitations

Training and Evaluation Data

Training Procedure

Hardware Environment:

Training results

Framework versions

ihebaker10/
spark-name-de-to-en