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# xlm-roberta-large-TASTESet-ner

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4970
- Precision: 0.8662
- Recall: 0.8989
- F1: 0.8822
- Accuracy: 0.8889

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 31   | 1.8592          | 0.3077    | 0.4305 | 0.3589 | 0.4376   |
| No log        | 2.0   | 62   | 1.3188          | 0.4793    | 0.5445 | 0.5098 | 0.5884   |
| No log        | 3.0   | 93   | 1.1581          | 0.5382    | 0.6134 | 0.5733 | 0.6391   |
| No log        | 4.0   | 124  | 1.1373          | 0.6480    | 0.5964 | 0.6211 | 0.6522   |
| No log        | 5.0   | 155  | 0.8784          | 0.6969    | 0.7370 | 0.7164 | 0.7425   |
| No log        | 6.0   | 186  | 0.7242          | 0.7472    | 0.7823 | 0.7643 | 0.7930   |
| No log        | 7.0   | 217  | 0.6340          | 0.7869    | 0.8258 | 0.8058 | 0.8225   |
| No log        | 8.0   | 248  | 0.5766          | 0.7832    | 0.8562 | 0.8180 | 0.8391   |
| No log        | 9.0   | 279  | 0.5200          | 0.8087    | 0.8702 | 0.8383 | 0.8583   |
| No log        | 10.0  | 310  | 0.4981          | 0.8495    | 0.8722 | 0.8607 | 0.8642   |
| No log        | 11.0  | 341  | 0.4732          | 0.8510    | 0.8836 | 0.8670 | 0.8762   |
| No log        | 12.0  | 372  | 0.4884          | 0.8593    | 0.8801 | 0.8696 | 0.8746   |
| No log        | 13.0  | 403  | 0.4701          | 0.8444    | 0.8893 | 0.8663 | 0.8825   |
| No log        | 14.0  | 434  | 0.4759          | 0.8576    | 0.8898 | 0.8734 | 0.8814   |
| No log        | 15.0  | 465  | 0.4765          | 0.8596    | 0.8945 | 0.8767 | 0.8840   |
| No log        | 16.0  | 496  | 0.4817          | 0.8610    | 0.8984 | 0.8793 | 0.8881   |
| 0.7221        | 17.0  | 527  | 0.4904          | 0.8572    | 0.8989 | 0.8775 | 0.8869   |
| 0.7221        | 18.0  | 558  | 0.4971          | 0.8640    | 0.8969 | 0.8802 | 0.8869   |
| 0.7221        | 19.0  | 589  | 0.4954          | 0.8595    | 0.9024 | 0.8804 | 0.8894   |
| 0.7221        | 20.0  | 620  | 0.4970          | 0.8662    | 0.8989 | 0.8822 | 0.8889   |


### Framework versions

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

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

xlm-roberta-large-TASTESet-ner
==============================


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


* Loss: 0.4970
* Precision: 0.8662
* Recall: 0.8989
* F1: 0.8822
* Accuracy: 0.8889


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: 20


### 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: 20 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #xlm-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: 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: 20 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #xlm-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: 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: 20### Training results### Framework versions


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