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# xlm-roberta-base-mapa_coarse-ner

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the lextreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0515
- Precision: 0.6624
- Recall: 0.6657
- F1: 0.6640
- Accuracy: 0.9872

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0584        | 1.0   | 1739  | 0.0576          | 0.6088    | 0.5790 | 0.5935 | 0.9860   |
| 0.0475        | 2.0   | 3478  | 0.0522          | 0.6455    | 0.6574 | 0.6514 | 0.9870   |
| 0.0409        | 3.0   | 5217  | 0.0517          | 0.6490    | 0.6675 | 0.6581 | 0.9871   |
| 0.04          | 4.0   | 6956  | 0.0516          | 0.6562    | 0.6720 | 0.6640 | 0.9871   |
| 0.0422        | 5.0   | 8695  | 0.0513          | 0.6573    | 0.6722 | 0.6647 | 0.9871   |
| 0.0398        | 6.0   | 10434 | 0.0515          | 0.6602    | 0.6697 | 0.6649 | 0.9872   |
| 0.0407        | 7.0   | 12173 | 0.0516          | 0.6612    | 0.6663 | 0.6638 | 0.9872   |
| 0.0382        | 8.0   | 13912 | 0.0516          | 0.6626    | 0.6648 | 0.6637 | 0.9872   |
| 0.0398        | 9.0   | 15651 | 0.0515          | 0.6627    | 0.6660 | 0.6643 | 0.9872   |
| 0.0401        | 10.0  | 17390 | 0.0515          | 0.6624    | 0.6657 | 0.6640 | 0.9872   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["lextreme"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "xlm-roberta-base-mapa_coarse-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "lextreme", "type": "lextreme", "config": "mapa_coarse", "split": "test", "args": "mapa_coarse"}, "metrics": [{"type": "precision", "value": 0.6624395127648923, "name": "Precision"}, {"type": "recall", "value": 0.6656606304493629, "name": "Recall"}, {"type": "f1", "value": 0.6640461654261103, "name": "F1"}, {"type": "accuracy", "value": 0.9872255987419513, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard xlm-roberta token-classification generated_from_trainer dataset:lextreme license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-lextreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

xlm-roberta-base-mapa\_coarse-ner
=================================


This model is a fine-tuned version of xlm-roberta-base on the lextreme dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0515
* Precision: 0.6624
* Recall: 0.6657
* F1: 0.6640
* Accuracy: 0.9872


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-lextreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-lextreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2