We evaluate the last 5 checkpoints (1M, 951k, 901k, 851k and 851k) with a recent version of ScandEval to check their performance and also compare it with popular encoder-only models such as BERT, RoBERTa or ELECTRA:

Model ID	Avg. Score	CoNLL-En	SST5	ScaLA-En	SQuAD
model-garden-lms/bert-base-finewebs-1m	69.03	88.98 ± 0.43 / 88.67 ± 0.36	58.11 ± 1.2 / 59.77 ± 1.49	57.29 ± 3.57 / 77.15 ± 2.17	55.82 ± 1.35 / 66.46 ± 1.51
model-garden-lms/bert-base-finewebs-951k	69.41	89.25 ± 0.4 / 88.9 ± 0.37	58.17 ± 1.26 / 59.86 ± 1.65	58.83 ± 3.46 / 78.22 ± 2.11	55.66 ± 1.19 / 66.36 ± 1.42
model-garden-lms/bert-base-finewebs-901k	69.12	89.22 ± 0.69 / 88.97 ± 0.45	57.93 ± 1.1 / 59.49 ± 1.44	58.66 ± 2.99 / 77.94 ± 1.88	55.0 ± 1.05 / 65.75 ± 1.29
model-garden-lms/bert-base-finewebs-851k	68.76	89.29 ± 0.52 / 89.0 ± 0.51	57.68 ± 0.97 / 59.01 ± 1.23	57.11 ± 3.77 / 77.36 ± 1.97	54.79 ± 1.21 / 65.87 ± 1.32
model-garden-lms/bert-base-finewebs-801k	68.12	88.92 ± 0.45 / 88.6 ± 0.44	57.64 ± 1.09 / 60.8 ± 1.88	54.28 ± 4.83 / 75.48 ± 2.97	54.13 ± 1.61 / 65.09 ± 1.65
google-bert/bert-base-cased	62.26	87.39 ± 0.79 / 87.11 ± 0.66	54.49 ± 1.36 / 53.22 ± 1.15	52.08 ± 2.13 / 74.52 ± 1.31	38.63 ± 2.1 / 50.68 ± 1.87
google/electra-base-discriminator	69.26	87.82 ± 0.69 / 86.83 ± 0.62	62.3 ± 1.12 / 55.93 ± 0.67	62.61 ± 1.21 / 80.85 ± 0.59	52.51 ± 0.86 / 65.2 ± 0.85
FacebookAI/roberta-base	68.96	90.35 ± 0.23 / 90.14 ± 0.2	60.95 ± 1.4 / 57.52 ± 1.97	50.64 ± 1.69 / 74.55 ± 0.9	57.82 ± 1.35 / 69.68 ± 1.02

Our pretrained BERT model shows a strong performance across all tasks. All detailed results can be found in this dataset repository.

❤️ Acknowledgements

This repository is the outcome of the last two years of working with TPUs from the awesome TRC program and the TensorFlow Model Garden library.

Made from Bavarian Oberland with ❤️ and 🥨.

model-garden-lms/bert-base-finewebs-901k

FineWeb-LMs: BERT

Pretraining Details

Checkpoint Evaluation with ScandEval

❤️ Acknowledgements

model-garden-lms/
bert-base-finewebs-901k