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# distilbert-finetuned-mit-restaurant-ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the mit_restaurant dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3210
- Precision: 0.7768
- Recall: 0.7983
- F1: 0.7874
- Accuracy: 0.9116

## 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: 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6991        | 1.0   | 863  | 0.3478          | 0.7113    | 0.7684 | 0.7387 | 0.8994   |
| 0.2773        | 2.0   | 1726 | 0.3264          | 0.7533    | 0.7989 | 0.7754 | 0.9063   |
| 0.2164        | 3.0   | 2589 | 0.3137          | 0.7644    | 0.8045 | 0.7839 | 0.9121   |
| 0.1789        | 4.0   | 3452 | 0.3163          | 0.7755    | 0.7983 | 0.7867 | 0.9115   |
| 0.1573        | 5.0   | 4315 | 0.3210          | 0.7768    | 0.7983 | 0.7874 | 0.9116   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mit_restaurant"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-finetuned-mit-restaurant-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mit_restaurant", "type": "mit_restaurant", "config": "mit_restaurant", "split": "validation", "args": "mit_restaurant"}, "metrics": [{"type": "precision", "value": 0.776800439802089, "name": "Precision"}, {"type": "recall", "value": 0.7983050847457627, "name": "Recall"}, {"type": "f1", "value": 0.7874059626636947, "name": "F1"}, {"type": "accuracy", "value": 0.9116093286947559, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer dataset:mit_restaurant license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-mit_restaurant #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert-finetuned-mit-restaurant-ner
=======================================


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


* Loss: 0.3210
* Precision: 0.7768
* Recall: 0.7983
* F1: 0.7874
* Accuracy: 0.9116


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: 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: 5


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 5 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-mit_restaurant #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 5 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-mit_restaurant #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 5### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2