metadata

license: mit
base_model: vicgalle/xlm-roberta-large-xnli-anli
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: xlm-roberta-large-xnli-anli-v5.0
    results: []

xlm-roberta-large-xnli-anli-v5.0

This model is a fine-tuned version of vicgalle/xlm-roberta-large-xnli-anli on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5120
F1 Macro: 0.8215
F1 Micro: 0.8223
Accuracy Balanced: 0.8216
Accuracy: 0.8223
Precision Macro: 0.8215
Recall Macro: 0.8216
Precision Micro: 0.8223
Recall Micro: 0.8223

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: 9e-06
train_batch_size: 8
eval_batch_size: 64
seed: 40
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.06
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Micro	Accuracy Balanced	Accuracy	Precision Macro	Recall Macro	Precision Micro	Recall Micro
0.3779	0.85	200	0.4494	0.8020	0.8020	0.8084	0.8020	0.8088	0.8084	0.8020	0.8020
0.2646	1.69	400	0.4425	0.8113	0.8121	0.8126	0.8121	0.8108	0.8126	0.8121	0.8121
0.1961	2.54	600	0.5222	0.8131	0.8147	0.8129	0.8147	0.8135	0.8129	0.8147	0.8147

eval result

Datasets	asadfgglie/nli-zh-tw-all/test	asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test	eval_dataset	test_dataset
eval_loss	0.541	0.26	0.517	0.512
eval_f1_macro	0.809	0.918	0.814	0.822
eval_f1_micro	0.81	0.918	0.815	0.822
eval_accuracy_balanced	0.809	0.918	0.815	0.822
eval_accuracy	0.81	0.918	0.815	0.822
eval_precision_macro	0.809	0.918	0.814	0.821
eval_recall_macro	0.809	0.918	0.815	0.822
eval_precision_micro	0.81	0.918	0.815	0.822
eval_recall_micro	0.81	0.918	0.815	0.822
eval_runtime	50.716	0.611	11.113	44.249
eval_samples_per_second	167.6	1548.868	169.977	170.785
eval_steps_per_second	2.622	24.559	2.699	2.689
Size of dataset	8500	946	1889	7557

Framework versions

Transformers 4.33.3
Pytorch 2.5.1+cu121
Datasets 2.14.7
Tokenizers 0.13.3