Flamenco43 / NanoBERT-V2

NanoBERT_V2

This model was pre-trained from scratch using the same model architecture as google-bert/bert-base-uncased .

A custom tokenizer was used, created using wordpiece from a corpus consisting of 200,000 domain-specific papers and a cut of BookCorpus.

Intended uses & limitations

Intended for training on downstream tasks using Nanoscience datasets. Can be used directly to create dense vector representations for information retrieval.

Training and evaluation data

Trained using 2 nodes on Polaris: https://docs.alcf.anl.gov/polaris/hardware-overview/machine-overview/

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• total_train_batch_size: 256
• total_eval_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 1000000

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0
• Datasets 2.20.0
• Tokenizers 0.19.1