README.md

---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

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

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/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