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# distilbert-base-uncased-ner-mit-restaurant

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: 0.3097
- Precision: 0.7874
- Recall: 0.8104
- F1: 0.7988
- Accuracy: 0.9119

## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 431  | 0.4575          | 0.6220    | 0.6856 | 0.6523 | 0.8650   |
| 1.1705        | 2.0   | 862  | 0.3183          | 0.7747    | 0.7953 | 0.7848 | 0.9071   |
| 0.3254        | 3.0   | 1293 | 0.3163          | 0.7668    | 0.8021 | 0.7841 | 0.9058   |
| 0.2287        | 4.0   | 1724 | 0.3097          | 0.7874    | 0.8104 | 0.7988 | 0.9119   |


### Framework versions

- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mit_restaurant"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-ner-mit-restaurant", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "mit_restaurant", "type": "mit_restaurant"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9118988661540467}}]}]}
transformers pytorch distilbert token-classification generated_from_trainer en dataset:mit_restaurant license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #en #dataset-mit_restaurant #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-ner-mit-restaurant
==========================================


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: 0.3097
* Precision: 0.7874
* Recall: 0.8104
* F1: 0.7988
* Accuracy: 0.9119


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: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #en #dataset-mit_restaurant #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: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #en #dataset-mit_restaurant #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: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4### Training results### Framework versions


* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3