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# distilbert-base-uncased-TASTESet-ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [TASTESet](https://huggingface.co/datasets/dmargutierrez/TASTESet) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3816
- Precision: 0.8929
- Recall: 0.9229
- F1: 0.9076
- Accuracy: 0.9130

## 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   | 1.0797          | 0.6027    | 0.6903 | 0.6435 | 0.7063   |
| No log        | 2.0   | 62   | 0.6402          | 0.7681    | 0.8295 | 0.7976 | 0.8304   |
| No log        | 3.0   | 93   | 0.4899          | 0.8379    | 0.8789 | 0.8579 | 0.8728   |
| No log        | 4.0   | 124  | 0.4232          | 0.8716    | 0.8994 | 0.8853 | 0.8912   |
| No log        | 5.0   | 155  | 0.3883          | 0.8798    | 0.9043 | 0.8919 | 0.8992   |
| No log        | 6.0   | 186  | 0.3848          | 0.8769    | 0.9103 | 0.8933 | 0.9004   |
| No log        | 7.0   | 217  | 0.3684          | 0.8864    | 0.9123 | 0.8991 | 0.9046   |
| No log        | 8.0   | 248  | 0.3650          | 0.8930    | 0.9182 | 0.9054 | 0.9087   |
| No log        | 9.0   | 279  | 0.3628          | 0.8908    | 0.9197 | 0.9050 | 0.9096   |
| No log        | 10.0  | 310  | 0.3674          | 0.8933    | 0.9165 | 0.9047 | 0.9093   |
| No log        | 11.0  | 341  | 0.3668          | 0.8958    | 0.9177 | 0.9066 | 0.9120   |
| No log        | 12.0  | 372  | 0.3717          | 0.8904    | 0.9234 | 0.9066 | 0.9120   |
| No log        | 13.0  | 403  | 0.3693          | 0.8940    | 0.9197 | 0.9067 | 0.9126   |
| No log        | 14.0  | 434  | 0.3805          | 0.8913    | 0.9239 | 0.9073 | 0.9135   |
| No log        | 15.0  | 465  | 0.3788          | 0.8954    | 0.9202 | 0.9076 | 0.9123   |
| No log        | 16.0  | 496  | 0.3803          | 0.8935    | 0.9231 | 0.9081 | 0.9122   |
| 0.3275        | 17.0  | 527  | 0.3814          | 0.8918    | 0.9229 | 0.9071 | 0.9126   |
| 0.3275        | 18.0  | 558  | 0.3823          | 0.8921    | 0.9241 | 0.9079 | 0.9123   |
| 0.3275        | 19.0  | 589  | 0.3827          | 0.8928    | 0.9224 | 0.9074 | 0.9124   |
| 0.3275        | 20.0  | 620  | 0.3816          | 0.8929    | 0.9229 | 0.9076 | 0.9130   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
{"license": "apache-2.0", "tags": ["named_entity_recognition", "food_recipes_ner"], "datasets": ["dmargutierrez/TASTESet"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-TASTESet-ner", "results": []}]}
transformers pytorch distilbert token-classification named_entity_recognition food_recipes_ner dataset:dmargutierrez/TASTESet license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #token-classification #named_entity_recognition #food_recipes_ner #dataset-dmargutierrez/TASTESet #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-TASTESet-ner
====================================


This model is a fine-tuned version of distilbert-base-uncased on the TASTESet dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3816
* Precision: 0.8929
* Recall: 0.9229
* F1: 0.9076
* Accuracy: 0.9130


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 #distilbert #token-classification #named_entity_recognition #food_recipes_ner #dataset-dmargutierrez/TASTESet #license-apache-2.0 #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 #distilbert #token-classification #named_entity_recognition #food_recipes_ner #dataset-dmargutierrez/TASTESet #license-apache-2.0 #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