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contemmcm/
29b2fb76c07c099f36418ba7737b131c

generated_from_trainer

google-bert/bert-base-uncased

finetune

apache-2.0

text-embeddings-inference

endpoints_compatible

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29b2fb76c07c099f36418ba7737b131c

This model is a fine-tuned version of google-bert/bert-base-uncased on the nyu-mll/glue [qnli] dataset. It achieves the following results on the evaluation set:

Loss: 0.3937
Data Size: 1.0
Epoch Runtime: 154.3733
Accuracy: 0.8866
F1 Macro: 0.8866
Rouge1: 0.8866
Rouge2: 0.0
Rougel: 0.8866
Rougelsum: 0.8868

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 32
total_eval_batch_size: 32
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Data Size	Epoch Runtime	Accuracy	F1 Macro	Rouge1	Rougel	Rougelsum
No log	0	0	0.6995	0	2.8676	0.4831	0.4239	0.4831	0.4828	0.4828
No log	1	3273	0.7037	0.0078	4.3227	0.4972	0.3385	0.4976	0.4972	0.4974
0.0109	2	6546	0.4979	0.0156	5.4515	0.7739	0.7723	0.7739	0.7737	0.7737
0.5214	3	9819	0.4226	0.0312	7.7248	0.8121	0.8115	0.8123	0.8119	0.8119
0.4554	4	13092	0.3854	0.0625	12.3875	0.8346	0.8344	0.8347	0.8346	0.8344
0.376	5	16365	0.3977	0.125	21.5760	0.8261	0.8251	0.8260	0.8259	0.8265
0.3596	6	19638	0.3566	0.25	40.2803	0.8636	0.8632	0.8638	0.8638	0.8638
0.2869	7	22911	0.3093	0.5	77.8252	0.8763	0.8761	0.8763	0.8763	0.8764
0.257	8.0	26184	0.3166	1.0	151.0262	0.8807	0.8805	0.8811	0.8809	0.8807
0.1795	9.0	29457	0.3467	1.0	158.1408	0.8849	0.8849	0.8851	0.8849	0.8849
0.1289	10.0	32730	0.4403	1.0	153.2826	0.8842	0.8841	0.8844	0.8844	0.8842
0.1383	11.0	36003	0.3937	1.0	154.3733	0.8866	0.8866	0.8866	0.8866	0.8868

Framework versions

Transformers 4.57.0
Pytorch 2.8.0+cu128
Datasets 4.3.0
Tokenizers 0.22.1

contemmcm/29b2fb76c07c099f36418ba7737b131c

29b2fb76c07c099f36418ba7737b131c

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

contemmcm/
29b2fb76c07c099f36418ba7737b131c