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# roberta-base-finetuned-ner

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0492
- Precision: 0.9530
- Recall: 0.9604
- F1: 0.9567
- Accuracy: 0.9889

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2031        | 1.0   | 878  | 0.0560          | 0.9381    | 0.9445 | 0.9413 | 0.9858   |
| 0.0446        | 2.0   | 1756 | 0.0480          | 0.9510    | 0.9578 | 0.9544 | 0.9887   |
| 0.0263        | 3.0   | 2634 | 0.0492          | 0.9530    | 0.9604 | 0.9567 | 0.9889   |


### Framework versions

- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.0
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9529566113766282, "name": "Precision"}, {"type": "recall", "value": 0.9604268983755194, "name": "Recall"}, {"type": "f1", "value": 0.9566771720212616, "name": "F1"}, {"type": "accuracy", "value": 0.988938664048357, "name": "Accuracy"}]}]}]}
transformers pytorch roberta token-classification generated_from_trainer dataset:conll2003 license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-finetuned-ner
==========================


This model is a fine-tuned version of roberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0492
* Precision: 0.9530
* Recall: 0.9604
* F1: 0.9567
* Accuracy: 0.9889


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: 3


### Training results



### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.0
* Tokenizers 0.10.3



### 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: 3 ### Training results ### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #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: 3 ### Training results ### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #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: 3### Training results### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.0
* Tokenizers 0.10.3