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contemmcm/
96529d1973d145b0ce126c03233b50f6

Name: 96529d1973d145b0ce126c03233b50f6
Rating: 2.5 (5 reviews)

generated_from_trainer

distilbert/distilroberta-base

finetune

apache-2.0

text-embeddings-inference

endpoints_compatible

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96529d1973d145b0ce126c03233b50f6

This model is a fine-tuned version of distilbert/distilroberta-base on the nyu-mll/glue [mnli] dataset. It achieves the following results on the evaluation set:

Loss: 0.6210
Data Size: 1.0
Epoch Runtime: 490.5435
Accuracy: 0.8114
F1 Macro: 0.8107
Rouge1: 0.8115
Rouge2: 0.0
Rougel: 0.8113
Rougelsum: 0.8115

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	1.1023	0	4.6905	0.3545	0.1747	0.3544	0.3545	0.3543
1.0933	1	12271	0.9008	0.0078	8.6237	0.6021	0.5969	0.6019	0.6023	0.6023
0.8248	2	24542	0.7477	0.0156	12.2017	0.6857	0.6797	0.6858	0.6860	0.6857
0.7031	3	36813	0.6315	0.0312	19.8785	0.7371	0.7345	0.7369	0.7373	0.7372
0.649	4	49084	0.5877	0.0625	34.7162	0.7629	0.7623	0.7625	0.7628	0.7627
0.5638	5	61355	0.5520	0.125	65.1321	0.7786	0.7767	0.7784	0.7789	0.7786
0.5506	6	73626	0.6088	0.25	125.5367	0.7786	0.7792	0.7786	0.7786	0.7786
0.4764	7	85897	0.5373	0.5	246.3156	0.7978	0.7971	0.7978	0.7981	0.7980
0.446	8.0	98168	0.5068	1.0	490.8175	0.8110	0.8100	0.8109	0.8110	0.8110
0.3883	9.0	110439	0.5302	1.0	486.9774	0.8090	0.8086	0.8091	0.8089	0.8089
0.3427	10.0	122710	0.5458	1.0	497.9757	0.8088	0.8077	0.8087	0.8088	0.8090
0.3202	11.0	134981	0.5834	1.0	487.6945	0.8106	0.8105	0.8104	0.8105	0.8108
0.254	12.0	147252	0.6210	1.0	490.5435	0.8114	0.8107	0.8115	0.8113	0.8115

Framework versions

Transformers 4.57.0
Pytorch 2.8.0+cu128
Datasets 4.3.0
Tokenizers 0.22.1

contemmcm/96529d1973d145b0ce126c03233b50f6

96529d1973d145b0ce126c03233b50f6

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

contemmcm/
96529d1973d145b0ce126c03233b50f6