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# xlnet-large-cased-ner-food-combined-weighted-v2

This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1182
- Precision: 0.7436
- Recall: 0.8947
- F1: 0.8122
- Accuracy: 0.9642

## 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: 5e-06
- train_batch_size: 16
- eval_batch_size: 24
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5366        | 1.12  | 500  | 0.1525          | 0.6775    | 0.8493 | 0.7537 | 0.9550   |
| 0.1697        | 2.25  | 1000 | 0.1385          | 0.6403    | 0.8580 | 0.7333 | 0.9457   |
| 0.1279        | 3.37  | 1500 | 0.1340          | 0.7899    | 0.8768 | 0.8311 | 0.9693   |
| 0.1178        | 4.49  | 2000 | 0.1247          | 0.7750    | 0.8876 | 0.8275 | 0.9679   |
| 0.1021        | 5.62  | 2500 | 0.1182          | 0.7436    | 0.8947 | 0.8122 | 0.9642   |
| 0.0957        | 6.74  | 3000 | 0.1192          | 0.7344    | 0.8876 | 0.8038 | 0.9626   |
| 0.0882        | 7.87  | 3500 | 0.1226          | 0.7641    | 0.8901 | 0.8223 | 0.9667   |
| 0.0802        | 8.99  | 4000 | 0.1323          | 0.7872    | 0.8901 | 0.8355 | 0.9695   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "xlnet-large-cased-ner-food-combined-weighted-v2", "results": []}]}
transformers pytorch tensorboard xlnet token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlnet #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

xlnet-large-cased-ner-food-combined-weighted-v2
===============================================


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


* Loss: 0.1182
* Precision: 0.7436
* Recall: 0.8947
* F1: 0.8122
* Accuracy: 0.9642


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: 5e-06
* train\_batch\_size: 16
* eval\_batch\_size: 24
* 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 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-06
* train\_batch\_size: 16
* eval\_batch\_size: 24
* 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 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlnet #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-06
* train\_batch\_size: 16
* eval\_batch\_size: 24
* 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 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlnet #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-06
* train\_batch\_size: 16
* eval\_batch\_size: 24
* 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 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3