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# xlm-roberta-base-david

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0697
- Precision: 0.9497
- Recall: 0.9544
- F1: 0.9520
- Accuracy: 0.9864

## 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: 2e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2104        | 0.1   | 100  | 0.6752          | 0.1279    | 0.0587 | 0.0804 | 0.7761   |
| 0.5384        | 0.2   | 200  | 0.3366          | 0.2616    | 0.2119 | 0.2341 | 0.8771   |
| 0.3168        | 0.3   | 300  | 0.2264          | 0.5493    | 0.4996 | 0.5233 | 0.9211   |
| 0.2345        | 0.39  | 400  | 0.1796          | 0.6662    | 0.8297 | 0.7390 | 0.9395   |
| 0.1883        | 0.49  | 500  | 0.1687          | 0.7203    | 0.8207 | 0.7672 | 0.9413   |
| 0.1587        | 0.59  | 600  | 0.1414          | 0.7661    | 0.8354 | 0.7992 | 0.9525   |
| 0.1605        | 0.69  | 700  | 0.1209          | 0.7946    | 0.8672 | 0.8293 | 0.9609   |
| 0.1365        | 0.79  | 800  | 0.1120          | 0.8304    | 0.8696 | 0.8495 | 0.9657   |
| 0.1205        | 0.89  | 900  | 0.1098          | 0.8659    | 0.8786 | 0.8722 | 0.9683   |
| 0.1353        | 0.99  | 1000 | 0.1239          | 0.8436    | 0.8794 | 0.8611 | 0.9643   |
| 0.1083        | 1.09  | 1100 | 0.1243          | 0.8537    | 0.8892 | 0.8711 | 0.9657   |
| 0.0961        | 1.18  | 1200 | 0.1078          | 0.8689    | 0.8965 | 0.8825 | 0.9696   |
| 0.0798        | 1.28  | 1300 | 0.0995          | 0.8774    | 0.9038 | 0.8904 | 0.9724   |
| 0.0843        | 1.38  | 1400 | 0.0965          | 0.8793    | 0.9144 | 0.8965 | 0.9733   |
| 0.0923        | 1.48  | 1500 | 0.0957          | 0.8815    | 0.9030 | 0.8921 | 0.9730   |
| 0.0847        | 1.58  | 1600 | 0.0959          | 0.8617    | 0.8941 | 0.8776 | 0.9709   |
| 0.089         | 1.68  | 1700 | 0.0844          | 0.8982    | 0.9201 | 0.9090 | 0.9760   |
| 0.0721        | 1.78  | 1800 | 0.0767          | 0.9       | 0.9095 | 0.9047 | 0.9782   |
| 0.0803        | 1.88  | 1900 | 0.0776          | 0.8981    | 0.9340 | 0.9157 | 0.9774   |
| 0.0766        | 1.97  | 2000 | 0.0611          | 0.9166    | 0.9315 | 0.9240 | 0.9816   |
| 0.0651        | 2.07  | 2100 | 0.0771          | 0.9127    | 0.9454 | 0.9287 | 0.9817   |
| 0.0562        | 2.17  | 2200 | 0.0908          | 0.9031    | 0.9112 | 0.9071 | 0.9771   |
| 0.0629        | 2.27  | 2300 | 0.0656          | 0.9184    | 0.9356 | 0.9269 | 0.9817   |
| 0.0504        | 2.37  | 2400 | 0.0836          | 0.8998    | 0.9299 | 0.9146 | 0.9775   |
| 0.0464        | 2.47  | 2500 | 0.0791          | 0.9310    | 0.9340 | 0.9325 | 0.9816   |
| 0.0396        | 2.57  | 2600 | 0.0763          | 0.9167    | 0.9234 | 0.9200 | 0.9816   |
| 0.0582        | 2.67  | 2700 | 0.0705          | 0.9198    | 0.9446 | 0.9320 | 0.9833   |
| 0.0561        | 2.76  | 2800 | 0.0635          | 0.9274    | 0.9470 | 0.9371 | 0.9835   |
| 0.0446        | 2.86  | 2900 | 0.0679          | 0.9301    | 0.9438 | 0.9369 | 0.9828   |
| 0.0429        | 2.96  | 3000 | 0.0663          | 0.9209    | 0.9397 | 0.9302 | 0.9820   |
| 0.0323        | 3.06  | 3100 | 0.0771          | 0.9303    | 0.9462 | 0.9382 | 0.9825   |
| 0.0228        | 3.16  | 3200 | 0.0839          | 0.9279    | 0.9446 | 0.9362 | 0.9830   |
| 0.0332        | 3.26  | 3300 | 0.0717          | 0.9365    | 0.9495 | 0.9429 | 0.9839   |
| 0.0351        | 3.36  | 3400 | 0.0668          | 0.9358    | 0.9381 | 0.9369 | 0.9840   |
| 0.0425        | 3.46  | 3500 | 0.0688          | 0.9363    | 0.9462 | 0.9412 | 0.9838   |
| 0.0431        | 3.55  | 3600 | 0.0710          | 0.9321    | 0.9503 | 0.9411 | 0.9840   |
| 0.0228        | 3.65  | 3700 | 0.0748          | 0.9343    | 0.9511 | 0.9426 | 0.9838   |
| 0.0334        | 3.75  | 3800 | 0.0770          | 0.9401    | 0.9462 | 0.9431 | 0.9834   |
| 0.0373        | 3.85  | 3900 | 0.0713          | 0.9294    | 0.9446 | 0.9369 | 0.9832   |
| 0.0368        | 3.95  | 4000 | 0.0668          | 0.9380    | 0.9495 | 0.9437 | 0.9845   |
| 0.0295        | 4.05  | 4100 | 0.0706          | 0.9364    | 0.9487 | 0.9425 | 0.9843   |
| 0.0169        | 4.15  | 4200 | 0.0675          | 0.9426    | 0.9503 | 0.9464 | 0.9863   |
| 0.0234        | 4.24  | 4300 | 0.0697          | 0.9497    | 0.9544 | 0.9520 | 0.9864   |
| 0.0235        | 4.34  | 4400 | 0.0713          | 0.9392    | 0.9576 | 0.9483 | 0.9857   |
| 0.0233        | 4.44  | 4500 | 0.0689          | 0.9428    | 0.9544 | 0.9486 | 0.9857   |
| 0.015         | 4.54  | 4600 | 0.0744          | 0.9404    | 0.9511 | 0.9457 | 0.9846   |
| 0.0154        | 4.64  | 4700 | 0.0753          | 0.9406    | 0.9552 | 0.9478 | 0.9860   |
| 0.0235        | 4.74  | 4800 | 0.0733          | 0.9431    | 0.9584 | 0.9507 | 0.9859   |
| 0.0239        | 4.84  | 4900 | 0.0728          | 0.9438    | 0.9576 | 0.9506 | 0.9864   |
| 0.0237        | 4.94  | 5000 | 0.0727          | 0.9437    | 0.9560 | 0.9498 | 0.9862   |


### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 2.11.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "xlm-roberta-base-david", "results": []}]}
transformers pytorch xlm-roberta token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

xlm-roberta-base-david
======================


This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0697
* Precision: 0.9497
* Recall: 0.9544
* F1: 0.9520
* Accuracy: 0.9864


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: 2e-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
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 1.10.1+cu113
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 1.10.1+cu113
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 1.10.1+cu113
* Datasets 2.11.0
* Tokenizers 0.13.3