transformers safetensors lilt generated_from_trainer dataset:xfun base_model:nielsr/lilt-xlm-roberta-base license:mit endpoints_compatible region:us
---
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
model-index:
- name: checkpoints
  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. -->

# checkpoints

This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.4372
- Recall: 0.6574
- F1: 0.5252
- Loss: 0.0001

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 10000

### Training results

| Training Loss | Epoch | Step  | F1     | Validation Loss | Precision | Recall |
|:-------------:|:-----:|:-----:|:------:|:---------------:|:---------:|:------:|
| 0.1954        | 20.0  | 500   | 0      | 0.4094          | 0         | 0      |
| 0.1588        | 40.0  | 1000  | 0.1420 | 0.3055          | 0.3587    | 0.0886 |
| 0.1182        | 60.0  | 1500  | 0.4253 | 0.1384          | 0.3810    | 0.4812 |
| 0.0477        | 80.0  | 2000  | 0.4764 | 0.0216          | 0.3949    | 0.6002 |
| 0.069         | 100.0 | 2500  | 0.5198 | 0.0115          | 0.4564    | 0.6038 |
| 0.0355        | 120.0 | 3000  | 0.5161 | 0.0018          | 0.4271    | 0.6521 |
| 0.0268        | 140.0 | 3500  | 0.5254 | 0.0016          | 0.4395    | 0.6530 |
| 0.0123        | 160.0 | 4000  | 0.5264 | 0.0015          | 0.4382    | 0.6592 |
| 0.0039        | 180.0 | 4500  | 0.5353 | 0.0011          | 0.4510    | 0.6583 |
| 0.0139        | 200.0 | 5000  | 0.5390 | 0.0011          | 0.4533    | 0.6646 |
| 0.001         | 220.0 | 5500  | 0.5430 | 0.0042          | 0.4620    | 0.6583 |
| 0.01          | 240.0 | 6000  | 0.5347 | 0.0013          | 0.4531    | 0.6521 |
| 0.0065        | 260.0 | 6500  | 0.5404 | 0.0001          | 0.4540    | 0.6673 |
| 0.0046        | 280.0 | 7000  | 0.5252 | 0.0001          | 0.4372    | 0.6574 |
| 0.002         | 300.0 | 7500  | 0.5365 | 0.0007          | 0.4474    | 0.6699 |
| 0.0002        | 320.0 | 8000  | 0.5393 | 0.0002          | 0.4546    | 0.6628 |
| 0.0008        | 340.0 | 8500  | 0.5412 | 0.0002          | 0.4569    | 0.6637 |
| 0.0024        | 360.0 | 9000  | 0.4677 | 0.6601          | 0.5475    | 0.0002 |
| 0.0001        | 380.0 | 9500  | 0.4560 | 0.6673          | 0.5418    | 0.0002 |
| 0.002         | 400.0 | 10000 | 0.4594 | 0.6628          | 0.5427    | 0.0003 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
