
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# XLMR-ENIS-finetuned-ner

This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0938
- Precision: 0.8619
- Recall: 0.8384
- F1: 0.8500
- Accuracy: 0.9831

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0574        | 1.0   | 2904 | 0.0983          | 0.8374    | 0.8061 | 0.8215 | 0.9795   |
| 0.0321        | 2.0   | 5808 | 0.0991          | 0.8525    | 0.8235 | 0.8378 | 0.9811   |
| 0.0179        | 3.0   | 8712 | 0.0938          | 0.8619    | 0.8384 | 0.8500 | 0.9831   |


### Framework versions

- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3

{"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "B\u00f3nus fe\u00f0garnir J\u00f3hannes J\u00f3nsson og J\u00f3n \u00c1sgeir J\u00f3hannesson opnu\u00f0u fyrstu B\u00f3nusb\u00fa\u00f0ina \u00ed 400 fermetra h\u00fasn\u00e6\u00f0i vi\u00f0 Sk\u00fatuvog laugardaginn 8. apr\u00edl 1989"}], "model-index": [{"name": "XLMR-ENIS-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "type": "mim_gold_ner", "args": "mim-gold-ner"}, "metrics": [{"type": "precision", "value": 0.861851332398317, "name": "Precision"}, {"type": "recall", "value": 0.8384309266628767, "name": "Recall"}, {"type": "f1", "value": 0.849979828251974, "name": "F1"}, {"type": "accuracy", "value": 0.9830620929487668, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard xlm-roberta token-classification generated_from_trainer dataset:mim_gold_ner license:agpl-3.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #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 mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0938
* Precision: 0.8619
* Recall: 0.8384
* F1: 0.8500
* Accuracy: 0.9831


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 4.11.2
* 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 4.11.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #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 4.11.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #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 4.11.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3