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contemmcm/
561f15e65d9e238c65bf9fe0731f5c3d

Name: 561f15e65d9e238c65bf9fe0731f5c3d
Rating: 2.5 (6 reviews)

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561f15e65d9e238c65bf9fe0731f5c3d

This model is a fine-tuned version of google-bert/bert-large-cased-whole-word-masking-finetuned-squad on the nyu-mll/glue [mrpc] dataset. It achieves the following results on the evaluation set:

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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 Loss	Epoch	Step	Validation Loss	Data Size	Epoch Runtime	Accuracy	F1 Macro	Rouge1	Rougel	Rougelsum
No log	0	0	0.9726	0	1.8402	0.3349	0.2509	0.3343	0.3355	0.3349
No log	1	114	0.6902	0.0078	2.8097	0.6651	0.3994	0.6657	0.6645	0.6651
No log	2	228	0.6867	0.0156	2.4793	0.6651	0.3994	0.6657	0.6645	0.6651
No log	3	342	0.6301	0.0312	3.2917	0.6651	0.3994	0.6657	0.6645	0.6651
0.0217	4	456	0.7452	0.0625	4.2934	0.3933	0.3506	0.3927	0.3933	0.3927
0.0217	5	570	0.6180	0.125	5.2000	0.7028	0.5445	0.7028	0.7022	0.7025
0.0217	6	684	0.5728	0.25	6.3172	0.7123	0.5617	0.7123	0.7117	0.7129
0.137	7	798	0.4067	0.5	9.1804	0.8208	0.8043	0.8213	0.8205	0.8205
0.4052	8.0	912	0.4089	1.0	14.8530	0.8367	0.8072	0.8373	0.8373	0.8367
0.2733	9.0	1026	0.4101	1.0	14.8673	0.8031	0.7913	0.8037	0.8031	0.8037
0.322	10.0	1140	0.5937	1.0	13.6805	0.7930	0.7285	0.7936	0.7936	0.7936
0.3307	11.0	1254	0.4758	1.0	13.8201	0.8231	0.8049	0.8231	0.8231	0.8231