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: 0.3911
- Recall: 0.6703
- F1: 0.4940
- Loss: 0.1352

## 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: 6
- 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 |
|:-------------:|:------:|:-----:|:------:|:---------------:|:---------:|:------:|
| 0.1469        | 20.83  | 500   | 0      | 0.1467          | 0         | 0      |
| 0.0896        | 41.67  | 1000  | 0.0837 | 0.1454          | 0.2946    | 0.0487 |
| 0.1027        | 62.5   | 1500  | 0.1225 | 0.1353          | 0.3333    | 0.0750 |
| 0.0485        | 83.33  | 2000  | 0.3536 | 0.1571          | 0.3364    | 0.3727 |
| 0.0597        | 104.17 | 2500  | 0.4448 | 0.1546          | 0.3535    | 0.5997 |
| 0.0367        | 125.0  | 3000  | 0.4940 | 0.1352          | 0.3911    | 0.6703 |
| 0.033         | 145.83 | 3500  | 0.4977 | 0.1749          | 0.3902    | 0.6870 |
| 0.0176        | 166.67 | 4000  | 0.5087 | 0.2262          | 0.4034    | 0.6883 |
| 0.0123        | 187.5  | 4500  | 0.5050 | 0.2358          | 0.3978    | 0.6915 |
| 0.0194        | 208.33 | 5000  | 0.5173 | 0.2976          | 0.4090    | 0.7037 |
| 0.0118        | 171.88 | 5500  | 0.4159 | 0.6863          | 0.5179    | 0.2836 |
| 0.0054        | 187.5  | 6000  | 0.4356 | 0.6703          | 0.5280    | 0.3100 |
| 0.01          | 203.12 | 6500  | 0.4229 | 0.6979          | 0.5266    | 0.3430 |
| 0.0062        | 218.75 | 7000  | 0.4272 | 0.7062          | 0.5324    | 0.3652 |
| 0.0051        | 234.38 | 7500  | 0.4306 | 0.6947          | 0.5317    | 0.3496 |
| 0.0048        | 250.0  | 8000  | 0.4400 | 0.6940          | 0.5386    | 0.3943 |
| 0.0087        | 265.62 | 8500  | 0.4290 | 0.6992          | 0.5317    | 0.3782 |
| 0.0077        | 281.25 | 9000  | 0.4394 | 0.7049          | 0.5414    | 0.3855 |
| 0.0014        | 296.88 | 9500  | 0.4363 | 0.7004          | 0.5377    | 0.3933 |
| 0.0035        | 312.5  | 10000 | 0.4350 | 0.6992          | 0.5363    | 0.4045 |


### Framework versions

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