
<!-- 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. -->

# xlm-roberta-large-WNUT-ner

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3570
- Precision: 0.7014
- Recall: 0.5116
- F1: 0.5916
- Accuracy: 0.9570

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.2223          | 0.5588    | 0.4495 | 0.4982 | 0.9504   |
| No log        | 2.0   | 426  | 0.2326          | 0.6602    | 0.4430 | 0.5302 | 0.9514   |
| 0.1516        | 3.0   | 639  | 0.2792          | 0.6846    | 0.4124 | 0.5147 | 0.9520   |
| 0.1516        | 4.0   | 852  | 0.2417          | 0.6510    | 0.5134 | 0.5741 | 0.9574   |
| 0.0427        | 5.0   | 1065 | 0.2954          | 0.6850    | 0.4856 | 0.5683 | 0.9544   |
| 0.0427        | 6.0   | 1278 | 0.3033          | 0.6761    | 0.4893 | 0.5677 | 0.9557   |
| 0.0427        | 7.0   | 1491 | 0.3502          | 0.7007    | 0.4838 | 0.5724 | 0.9563   |
| 0.0178        | 8.0   | 1704 | 0.3712          | 0.6995    | 0.4875 | 0.5745 | 0.9563   |
| 0.0178        | 9.0   | 1917 | 0.3541          | 0.6951    | 0.4986 | 0.5807 | 0.9569   |
| 0.0068        | 10.0  | 2130 | 0.3570          | 0.7014    | 0.5116 | 0.5916 | 0.9570   |


### 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"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "xlm-roberta-large-WNUT-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "type": "wnut_17", "config": "wnut_17", "split": "test", "args": "wnut_17"}, "metrics": [{"type": "precision", "value": 0.7013977128335451, "name": "Precision"}, {"type": "recall", "value": 0.5115848007414272, "name": "Recall"}, {"type": "f1", "value": 0.5916398713826366, "name": "F1"}, {"type": "accuracy", "value": 0.9570402667350603, "name": "Accuracy"}]}]}]}
transformers pytorch xlm-roberta token-classification generated_from_trainer dataset:wnut_17 license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-wnut_17 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

xlm-roberta-large-WNUT-ner
==========================


This model is a fine-tuned version of xlm-roberta-large on the wnut\_17 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3570
* Precision: 0.7014
* Recall: 0.5116
* F1: 0.5916
* Accuracy: 0.9570


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: 10


### 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: 10 ### 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 #dataset-wnut_17 #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: 10 ### 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 #dataset-wnut_17 #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: 10### Training results### Framework versions


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