README.md

metadata

library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - rouge
model-index:
  - name: 7f46eeb9fff60eed8b49f05e77ab5d1d
    results: []

7f46eeb9fff60eed8b49f05e77ab5d1d

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

• Loss: 0.6347
• Data Size: 1.0
• Epoch Runtime: 616.6585
• Accuracy: 0.7639
• F1 Macro: 0.7624
• Rouge1: 0.7638
• Rouge2: 0.0
• Rougel: 0.7639
• Rougelsum: 0.7642

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	Rouge2	Rougel	Rougelsum
No log	0	0	1.1021	0	4.8366	0.3333	0.2588	0.3333	0.0	0.3333	0.3332
1.056	1	12271	0.9014	0.0078	9.7280	0.5923	0.5927	0.5924	0.0	0.5925	0.5928
0.8885	2	24542	0.8165	0.0156	15.4293	0.6421	0.6365	0.6422	0.0	0.6425	0.6423
0.8231	3	36813	0.7632	0.0312	24.1737	0.6675	0.6626	0.6675	0.0	0.6675	0.6675
0.7834	4	49084	0.7125	0.0625	43.9368	0.6962	0.6953	0.6964	0.0	0.6962	0.6965
0.6754	5	61355	0.6893	0.125	82.9030	0.7145	0.7108	0.7144	0.0	0.7144	0.7148
0.6879	6	73626	0.6884	0.25	161.7223	0.7211	0.7215	0.7214	0.0	0.7212	0.7210
0.5822	7	85897	0.6155	0.5	318.7635	0.7488	0.7480	0.7486	0.0	0.7487	0.7489
0.575	8.0	98168	0.5989	1.0	631.4860	0.7701	0.7691	0.7699	0.0	0.7701	0.7701
0.4987	9.0	110439	0.6014	1.0	619.4724	0.7711	0.7704	0.7711	0.0	0.7712	0.7711
0.4853	10.0	122710	0.5948	1.0	612.5831	0.7719	0.7704	0.7720	0.0	0.7719	0.7720
0.4152	11.0	134981	0.6218	1.0	614.1248	0.7623	0.7630	0.7623	0.0	0.7624	0.7623
0.4063	12.0	147252	0.6741	1.0	614.9903	0.7557	0.7546	0.7555	0.0	0.7558	0.7559
0.4303	13.0	159523	0.7124	1.0	617.9320	0.7563	0.7572	0.7562	0.0	0.7566	0.7564
0.3838	14.0	171794	0.6347	1.0	616.6585	0.7639	0.7624	0.7638	0.0	0.7639	0.7642

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.3.0
• Tokenizers 0.22.1