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contemmcm/
4eeb84ca882950d4b363c0c46a2e4727

Name: 4eeb84ca882950d4b363c0c46a2e4727
Rating: 2.5 (6 reviews)

generated_from_trainer

google-bert/bert-large-uncased

finetune

apache-2.0

text-embeddings-inference

endpoints_compatible

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4eeb84ca882950d4b363c0c46a2e4727

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

Loss: 1.1049
Data Size: 1.0
Epoch Runtime: 1139.9687
Accuracy: 0.3182
F1 Macro: 0.1609
Rouge1: 0.3184
Rouge2: 0.0
Rougel: 0.3182
Rougelsum: 0.3183

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	1.1179	0	8.0435	0.3335	0.2694	0.3336	0.3333	0.3336
1.0795	1	12271	0.8507	0.0078	17.9024	0.6217	0.6221	0.6216	0.6218	0.6218
0.7539	2	24542	0.6441	0.0156	26.8720	0.7393	0.7385	0.7394	0.7395	0.7391
0.6306	3	36813	0.5904	0.0312	44.8434	0.7605	0.7573	0.7603	0.7605	0.7604
0.6071	4	49084	0.5601	0.0625	80.2704	0.7780	0.7780	0.7777	0.7780	0.7778
0.5475	5	61355	0.5343	0.125	148.3037	0.7894	0.7880	0.7893	0.7894	0.7893
0.5347	6	73626	0.5374	0.25	289.3529	0.7860	0.7863	0.7858	0.7863	0.7860
0.4735	7	85897	0.5119	0.5	561.7854	0.7961	0.7955	0.7962	0.7962	0.7960
1.1105	8.0	98168	1.0994	1.0	1129.3448	0.3182	0.1609	0.3184	0.3182	0.3183
1.1032	9.0	110439	1.1043	1.0	1132.9095	0.3182	0.1609	0.3184	0.3182	0.3183
1.1043	10.0	122710	1.1019	1.0	1141.1488	0.3273	0.1644	0.3273	0.3275	0.3277
1.1004	11.0	134981	1.1049	1.0	1139.9687	0.3182	0.1609	0.3184	0.3182	0.3183

Framework versions

Transformers 4.57.0
Pytorch 2.8.0+cu128
Datasets 4.3.0
Tokenizers 0.22.1

contemmcm/4eeb84ca882950d4b363c0c46a2e4727

4eeb84ca882950d4b363c0c46a2e4727

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

contemmcm/
4eeb84ca882950d4b363c0c46a2e4727