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# roberta-large-finetuned-ner

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1384
- Precision: 0.9616
- Recall: 0.9566
- F1: 0.9591
- Accuracy: 0.9548

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.167         | 0.25  | 7000   | 0.1616          | 0.9484    | 0.9366 | 0.9424 | 0.9376   |
| 0.1673        | 0.49  | 14000  | 0.1459          | 0.9504    | 0.9370 | 0.9437 | 0.9389   |
| 0.1472        | 0.74  | 21000  | 0.1560          | 0.9531    | 0.9373 | 0.9451 | 0.9398   |
| 0.1519        | 0.98  | 28000  | 0.1434          | 0.9551    | 0.9382 | 0.9466 | 0.9415   |
| 0.1388        | 1.23  | 35000  | 0.1472          | 0.9516    | 0.9374 | 0.9444 | 0.9400   |
| 0.1291        | 1.48  | 42000  | 0.1416          | 0.9557    | 0.9403 | 0.9479 | 0.9431   |
| 0.1298        | 1.72  | 49000  | 0.1394          | 0.9577    | 0.9459 | 0.9517 | 0.9470   |
| 0.1269        | 1.97  | 56000  | 0.1401          | 0.9587    | 0.9446 | 0.9516 | 0.9468   |
| 0.1128        | 2.21  | 63000  | 0.1410          | 0.9568    | 0.9497 | 0.9533 | 0.9486   |
| 0.1154        | 2.46  | 70000  | 0.1366          | 0.9583    | 0.9495 | 0.9539 | 0.9493   |
| 0.1138        | 2.71  | 77000  | 0.1413          | 0.9600    | 0.9502 | 0.9551 | 0.9506   |
| 0.1117        | 2.95  | 84000  | 0.1313          | 0.9605    | 0.9501 | 0.9552 | 0.9508   |
| 0.0997        | 3.2   | 91000  | 0.1503          | 0.9577    | 0.9527 | 0.9552 | 0.9507   |
| 0.1008        | 3.44  | 98000  | 0.1360          | 0.9587    | 0.9536 | 0.9561 | 0.9515   |
| 0.0909        | 3.69  | 105000 | 0.1435          | 0.9619    | 0.9520 | 0.9569 | 0.9525   |
| 0.0903        | 3.93  | 112000 | 0.1482          | 0.9619    | 0.9522 | 0.9570 | 0.9528   |
| 0.075         | 4.18  | 119000 | 0.1603          | 0.9616    | 0.9546 | 0.9581 | 0.9537   |
| 0.0804        | 4.43  | 126000 | 0.1512          | 0.9600    | 0.9560 | 0.9580 | 0.9536   |
| 0.0811        | 4.67  | 133000 | 0.1435          | 0.9628    | 0.9543 | 0.9585 | 0.9540   |
| 0.0778        | 4.92  | 140000 | 0.1384          | 0.9616    | 0.9566 | 0.9591 | 0.9548   |
| 0.065         | 5.16  | 147000 | 0.1640          | 0.9622    | 0.9567 | 0.9595 | 0.9550   |
| 0.0607        | 5.41  | 154000 | 0.1755          | 0.9632    | 0.9562 | 0.9597 | 0.9554   |
| 0.0587        | 5.66  | 161000 | 0.1643          | 0.9622    | 0.9575 | 0.9599 | 0.9555   |
| 0.062         | 5.9   | 168000 | 0.1663          | 0.9628    | 0.9569 | 0.9598 | 0.9556   |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-large-finetuned-ner", "results": []}]}
transformers pytorch roberta token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

roberta-large-finetuned-ner
===========================


This model is a fine-tuned version of roberta-large on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1384
* Precision: 0.9616
* Recall: 0.9566
* F1: 0.9591
* Accuracy: 0.9548


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: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6


### Training results



### Framework versions


* Transformers 4.16.2
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6### Training results### Framework versions


* Transformers 4.16.2
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.10.3