
<!-- 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. -->

# electra-base-discriminator-ner-food-combined-v2

This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1277
- Precision: 0.8006
- Recall: 0.8959
- F1: 0.8456
- Accuracy: 0.9685

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.45  | 400  | 0.1279          | 0.7429    | 0.8888 | 0.8093 | 0.9603   |
| 0.2005        | 0.9   | 800  | 0.1306          | 0.8145    | 0.8901 | 0.8506 | 0.9704   |
| 0.1305        | 1.35  | 1200 | 0.1197          | 0.7847    | 0.8951 | 0.8363 | 0.9667   |
| 0.1143        | 1.8   | 1600 | 0.1118          | 0.7876    | 0.8922 | 0.8366 | 0.9661   |
| 0.1169        | 2.25  | 2000 | 0.1125          | 0.7724    | 0.8959 | 0.8296 | 0.9647   |
| 0.1169        | 2.7   | 2400 | 0.1167          | 0.7964    | 0.8922 | 0.8415 | 0.9674   |
| 0.1007        | 3.15  | 2800 | 0.1222          | 0.8170    | 0.8905 | 0.8522 | 0.9708   |
| 0.1008        | 3.6   | 3200 | 0.1164          | 0.7732    | 0.8913 | 0.8281 | 0.9640   |
| 0.0973        | 4.04  | 3600 | 0.1190          | 0.8093    | 0.8993 | 0.8519 | 0.9697   |
| 0.0948        | 4.49  | 4000 | 0.1221          | 0.7977    | 0.8947 | 0.8434 | 0.9676   |
| 0.0948        | 4.94  | 4400 | 0.1220          | 0.8009    | 0.8993 | 0.8472 | 0.9684   |
| 0.0857        | 5.39  | 4800 | 0.1292          | 0.8085    | 0.8963 | 0.8501 | 0.9694   |
| 0.0845        | 5.84  | 5200 | 0.1318          | 0.8236    | 0.8943 | 0.8575 | 0.9710   |
| 0.0877        | 6.29  | 5600 | 0.1246          | 0.7940    | 0.8972 | 0.8425 | 0.9674   |
| 0.0825        | 6.74  | 6000 | 0.1277          | 0.8006    | 0.8959 | 0.8456 | 0.9685   |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "electra-base-discriminator-ner-food-combined-v2", "results": []}]}
transformers pytorch tensorboard electra token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

electra-base-discriminator-ner-food-combined-v2
===============================================


This model is a fine-tuned version of google/electra-base-discriminator on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1277
* Precision: 0.8006
* Recall: 0.8959
* F1: 0.8456
* Accuracy: 0.9685


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 7


### 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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 7 ### 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 #electra #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 7 ### 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 #electra #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 7### Training results### Framework versions


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