README.md

metadata

library_name: transformers
license: apache-2.0
base_model: albert/albert-xlarge-v2
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - rouge
model-index:
  - name: 4b7ba4f0e03f0f2b491648f64b36a49c
    results: []

4b7ba4f0e03f0f2b491648f64b36a49c

This model is a fine-tuned version of albert/albert-xlarge-v2 on the nyu-mll/glue dataset. It achieves the following results on the evaluation set:

• Loss: 1.0998
• Data Size: 1.0
• Epoch Runtime: 845.5666
• Accuracy: 0.3273
• F1 Macro: 0.1644
• Rouge1: 0.3273
• Rouge2: 0.0
• Rougel: 0.3275
• Rougelsum: 0.3277

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	1.1551	0	7.0618	0.3650	0.2893	0.3651	0.0	0.3648	0.3650
1.1413	1	12271	1.1101	0.0078	13.7628	0.3273	0.1644	0.3273	0.0	0.3275	0.3277
1.112	2	24542	1.1001	0.0156	20.1830	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.1113	3	36813	1.0988	0.0312	33.4603	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.1033	4	49084	1.0985	0.0625	59.5514	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.1021	5	61355	1.0972	0.125	112.1817	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.1	6	73626	1.1049	0.25	216.3272	0.3182	0.1609	0.3184	0.0	0.3182	0.3183
1.1003	7	85897	1.0987	0.5	426.1488	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.0981	8.0	98168	1.0986	1.0	846.6244	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.0994	9.0	110439	1.0998	1.0	845.5666	0.3273	0.1644	0.3273	0.0	0.3275	0.3277

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.1