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# XLMR-ENIS-finetuned-ner

This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0671
- Precision: 0.9398
- Recall: 0.9518
- F1: 0.9458
- Accuracy: 0.9854

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2825        | 1.0   | 878  | 0.0712          | 0.9220    | 0.9379 | 0.9299 | 0.9815   |
| 0.0688        | 2.0   | 1756 | 0.0689          | 0.9354    | 0.9477 | 0.9415 | 0.9839   |
| 0.039         | 3.0   | 2634 | 0.0671          | 0.9398    | 0.9518 | 0.9458 | 0.9854   |


### Framework versions

- Transformers 4.10.3
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3

{"language": ["en", "is", "multilingual"], "license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "vesteinn/XLMR-ENIS", "model-index": [{"name": "XLMR-ENIS-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9398313331170938, "name": "Precision"}, {"type": "recall", "value": 0.9517943664285128, "name": "Recall"}, {"type": "f1", "value": 0.9457750214207026, "name": "F1"}, {"type": "accuracy", "value": 0.9853686150987764, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard safetensors xlm-roberta token-classification generated_from_trainer en is multilingual dataset:conll2003 base_model:vesteinn/XLMR-ENIS license:agpl-3.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #generated_from_trainer #en #is #multilingual #dataset-conll2003 #base_model-vesteinn/XLMR-ENIS #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

XLMR-ENIS-finetuned-ner
=======================


This model is a fine-tuned version of vesteinn/XLMR-ENIS on the conll2003 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0671
* Precision: 0.9398
* Recall: 0.9518
* F1: 0.9458
* Accuracy: 0.9854


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 4.10.3
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* 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 4.10.3
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #generated_from_trainer #en #is #multilingual #dataset-conll2003 #base_model-vesteinn/XLMR-ENIS #license-agpl-3.0 #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: 3 ### Training results ### Framework versions


* Transformers 4.10.3
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #generated_from_trainer #en #is #multilingual #dataset-conll2003 #base_model-vesteinn/XLMR-ENIS #license-agpl-3.0 #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: 3### Training results### Framework versions


* Transformers 4.10.3
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3