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# roberta-base-mapa_fine-ner

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the lextreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0401
- Precision: 0.7395
- Recall: 0.8237
- F1: 0.7793
- Accuracy: 0.9917

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0877        | 1.0   | 1739  | 0.0495          | 0.6861    | 0.7595 | 0.7209 | 0.9903   |
| 0.0661        | 2.0   | 3478  | 0.0432          | 0.7278    | 0.8092 | 0.7663 | 0.9914   |
| 0.0633        | 3.0   | 5217  | 0.0403          | 0.7469    | 0.8128 | 0.7785 | 0.9919   |
| 0.059         | 4.0   | 6956  | 0.0401          | 0.7412    | 0.8196 | 0.7784 | 0.9918   |
| 0.063         | 5.0   | 8695  | 0.0400          | 0.7425    | 0.8200 | 0.7793 | 0.9918   |
| 0.0593        | 6.0   | 10434 | 0.0405          | 0.7332    | 0.8244 | 0.7761 | 0.9916   |
| 0.0595        | 7.0   | 12173 | 0.0400          | 0.7389    | 0.8222 | 0.7783 | 0.9917   |
| 0.0593        | 8.0   | 13912 | 0.0401          | 0.7390    | 0.8229 | 0.7787 | 0.9917   |
| 0.0594        | 9.0   | 15651 | 0.0402          | 0.7374    | 0.8240 | 0.7783 | 0.9917   |
| 0.0597        | 10.0  | 17390 | 0.0401          | 0.7395    | 0.8237 | 0.7793 | 0.9917   |


### 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": "roberta-base-mapa_fine-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "lextreme", "type": "lextreme", "config": "mapa_fine", "split": "test", "args": "mapa_fine"}, "metrics": [{"type": "precision", "value": 0.7395134779750164, "name": "Precision"}, {"type": "recall", "value": 0.8236672524897481, "name": "Recall"}, {"type": "f1", "value": 0.7793251576248873, "name": "F1"}, {"type": "accuracy", "value": 0.991740752278482, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard roberta token-classification generated_from_trainer dataset:lextreme license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-lextreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-mapa\_fine-ner
===========================


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


* Loss: 0.0401
* Precision: 0.7395
* Recall: 0.8237
* F1: 0.7793
* Accuracy: 0.9917


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 #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 #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