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# lilt-xlm-roberta-base-finetuned-funsd-iob-original

This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1573
- Precision: 0.7252
- Recall: 0.7718
- F1: 0.7478
- Accuracy: 0.7676

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.33  | 100  | 0.8309          | 0.5157    | 0.6673 | 0.5818 | 0.6594   |
| No log        | 2.67  | 200  | 1.0045          | 0.6080    | 0.6699 | 0.6374 | 0.7387   |
| No log        | 4.0   | 300  | 0.9127          | 0.6177    | 0.7310 | 0.6696 | 0.7427   |
| No log        | 5.33  | 400  | 0.9808          | 0.6478    | 0.7300 | 0.6865 | 0.7521   |
| 0.6318        | 6.67  | 500  | 1.2169          | 0.6863    | 0.7376 | 0.7110 | 0.7547   |
| 0.6318        | 8.0   | 600  | 1.1830          | 0.6918    | 0.7580 | 0.7234 | 0.7326   |
| 0.6318        | 9.33  | 700  | 1.3537          | 0.6955    | 0.7504 | 0.7219 | 0.7426   |
| 0.6318        | 10.67 | 800  | 1.3888          | 0.6994    | 0.7611 | 0.7290 | 0.7507   |
| 0.6318        | 12.0  | 900  | 1.5929          | 0.7204    | 0.7560 | 0.7378 | 0.7553   |
| 0.1082        | 13.33 | 1000 | 1.7679          | 0.6891    | 0.7397 | 0.7135 | 0.7452   |
| 0.1082        | 14.67 | 1100 | 1.7197          | 0.7003    | 0.7570 | 0.7275 | 0.7530   |
| 0.1082        | 16.0  | 1200 | 1.8053          | 0.7188    | 0.7448 | 0.7315 | 0.7616   |
| 0.1082        | 17.33 | 1300 | 1.9315          | 0.7109    | 0.7728 | 0.7405 | 0.7643   |
| 0.1082        | 18.67 | 1400 | 2.0142          | 0.7240    | 0.7789 | 0.7504 | 0.7676   |
| 0.0312        | 20.0  | 1500 | 2.0475          | 0.7264    | 0.7478 | 0.7369 | 0.7654   |
| 0.0312        | 21.33 | 1600 | 2.0463          | 0.7251    | 0.7539 | 0.7393 | 0.7599   |
| 0.0312        | 22.67 | 1700 | 2.0648          | 0.7289    | 0.7753 | 0.7514 | 0.7623   |
| 0.0312        | 24.0  | 1800 | 2.1301          | 0.7272    | 0.7606 | 0.7435 | 0.7667   |
| 0.0312        | 25.33 | 1900 | 2.1319          | 0.7274    | 0.7585 | 0.7426 | 0.7694   |
| 0.0064        | 26.67 | 2000 | 2.1499          | 0.7247    | 0.7723 | 0.7477 | 0.7673   |
| 0.0064        | 28.0  | 2100 | 2.1627          | 0.7235    | 0.7733 | 0.7476 | 0.7670   |
| 0.0064        | 29.33 | 2200 | 2.1573          | 0.7252    | 0.7718 | 0.7478 | 0.7676   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "lilt-xlm-roberta-base-finetuned-funsd-iob-original", "results": []}]}
transformers pytorch tensorboard lilt token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

lilt-xlm-roberta-base-finetuned-funsd-iob-original
==================================================


This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 2.1573
* Precision: 0.7252
* Recall: 0.7718
* F1: 0.7478
* Accuracy: 0.7676


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2