README.md

metadata

library_name: peft
license: mit
base_model: xlm-roberta-large
tags:
  - base_model:adapter:xlm-roberta-large
  - lora
  - transformers
metrics:
  - accuracy
model-index:
  - name: cil-noise-weight-q5-xlmr-large-seed1
    results: []

cil-noise-weight-q5-xlmr-large-seed1

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

• Loss: 0.7867
• Accuracy: 0.6623
• Map Mae: 0.3763
• Bayes Mae: 0.3746
• Expected Score Mae: 0.4254

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: 0.00015
• train_batch_size: 64
• eval_batch_size: 1024
• seed: 1
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Map Mae	Bayes Mae	Expected Score Mae
1.1054	0.1411	500	0.8717	0.6246	0.4273	0.4203	0.4875
0.8149	0.2822	1000	0.8269	0.6487	0.3873	0.3873	0.4555
0.7660	0.4233	1500	0.8029	0.6539	0.3825	0.3839	0.4438
0.7476	0.5643	2000	0.7872	0.6571	0.3860	0.3809	0.4351
0.7361	0.7054	2500	0.7922	0.6606	0.3796	0.3771	0.4285
0.7281	0.8465	3000	0.7943	0.6615	0.3771	0.3757	0.4254
0.7269	0.9876	3500	0.7867	0.6622	0.3763	0.3747	0.4254
0.7269	1.0	3544	0.7867	0.6623	0.3763	0.3746	0.4254

Framework versions

• PEFT 0.19.1
• Transformers 5.8.1
• Pytorch 2.11.0+cu128
• Datasets 4.8.5
• Tokenizers 0.22.2