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# distilroberta-base-mapa_coarse-ner

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the lextreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1020
- Precision: 0.7441
- Recall: 0.5805
- F1: 0.6522
- 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: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0343        | 1.0   | 1739  | 0.0694          | 0.6342    | 0.5205 | 0.5718 | 0.9841   |
| 0.0263        | 2.0   | 3478  | 0.0705          | 0.7961    | 0.5235 | 0.6317 | 0.9865   |
| 0.0183        | 3.0   | 5217  | 0.0670          | 0.7417    | 0.5313 | 0.6191 | 0.9864   |
| 0.015         | 4.0   | 6956  | 0.0632          | 0.7237    | 0.5850 | 0.6470 | 0.9869   |
| 0.0137        | 5.0   | 8695  | 0.0663          | 0.7311    | 0.6064 | 0.6629 | 0.9872   |
| 0.011         | 6.0   | 10434 | 0.0703          | 0.7163    | 0.5877 | 0.6457 | 0.9868   |
| 0.0096        | 7.0   | 12173 | 0.0799          | 0.7511    | 0.5676 | 0.6466 | 0.9871   |
| 0.0071        | 8.0   | 13912 | 0.0770          | 0.7386    | 0.5640 | 0.6396 | 0.9868   |
| 0.0068        | 9.0   | 15651 | 0.0827          | 0.7285    | 0.5674 | 0.6379 | 0.9868   |
| 0.0057        | 10.0  | 17390 | 0.0897          | 0.7611    | 0.5719 | 0.6531 | 0.9872   |
| 0.0053        | 11.0  | 19129 | 0.0940          | 0.7614    | 0.5627 | 0.6471 | 0.9871   |
| 0.004         | 12.0  | 20868 | 0.0874          | 0.7184    | 0.6084 | 0.6588 | 0.9873   |
| 0.0035        | 13.0  | 22607 | 0.0986          | 0.7513    | 0.5766 | 0.6525 | 0.9872   |
| 0.003         | 14.0  | 24346 | 0.1012          | 0.7396    | 0.5805 | 0.6505 | 0.9871   |
| 0.0026        | 15.0  | 26085 | 0.1020          | 0.7441    | 0.5805 | 0.6522 | 0.9872   |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["lextreme"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilroberta-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": 0.7440758293838863, "name": "Precision"}, {"type": "recall", "value": 0.5805042016806723, "name": "Recall"}, {"type": "f1", "value": 0.652190332326284, "name": "F1"}, {"type": "accuracy", "value": 0.9871584939520047, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard safetensors roberta token-classification generated_from_trainer dataset:lextreme license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #token-classification #generated_from_trainer #dataset-lextreme #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilroberta-base-mapa\_coarse-ner
===================================


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


* Loss: 0.1020
* Precision: 0.7441
* Recall: 0.5805
* F1: 0.6522
* 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: 15


### 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: 15 ### 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 #safetensors #roberta #token-classification #generated_from_trainer #dataset-lextreme #license-apache-2.0 #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: 15 ### 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 #safetensors #roberta #token-classification #generated_from_trainer #dataset-lextreme #license-apache-2.0 #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: 15### Training results### Framework versions


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