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# chope-fine-dishing-distilbert-base-uncased-finetuned-ner-v0.1

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4448
- Precision: 0.6106
- Recall: 0.7842
- F1: 0.6866
- Accuracy: 0.7525

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.46  | 50   | 0.8879          | 0.3658    | 0.2978 | 0.3283 | 0.5883   |
| No log        | 0.92  | 100  | 0.7451          | 0.5553    | 0.6995 | 0.6191 | 0.7048   |
| No log        | 1.38  | 150  | 0.7378          | 0.5351    | 0.6448 | 0.5849 | 0.7171   |
| No log        | 1.83  | 200  | 0.8367          | 0.6037    | 0.6202 | 0.6119 | 0.7012   |
| No log        | 2.29  | 250  | 0.7746          | 0.6328    | 0.6639 | 0.6480 | 0.7373   |
| No log        | 2.75  | 300  | 0.8077          | 0.5       | 0.5956 | 0.5436 | 0.6939   |
| No log        | 3.21  | 350  | 0.8416          | 0.5284    | 0.4836 | 0.5050 | 0.7012   |
| No log        | 3.67  | 400  | 0.9220          | 0.5601    | 0.7131 | 0.6274 | 0.7323   |
| No log        | 4.13  | 450  | 0.9337          | 0.5419    | 0.5301 | 0.5359 | 0.7113   |
| 0.2476        | 4.59  | 500  | 0.9225          | 0.6387    | 0.6667 | 0.6524 | 0.7323   |
| 0.2476        | 5.05  | 550  | 1.0376          | 0.5296    | 0.5383 | 0.5339 | 0.7033   |
| 0.2476        | 5.5   | 600  | 1.0138          | 0.5820    | 0.7760 | 0.6651 | 0.7496   |
| 0.2476        | 5.96  | 650  | 1.1675          | 0.6184    | 0.6421 | 0.6300 | 0.7366   |
| 0.2476        | 6.42  | 700  | 1.2386          | 0.5563    | 0.7022 | 0.6208 | 0.7272   |
| 0.2476        | 6.88  | 750  | 1.2480          | 0.6233    | 0.7322 | 0.6734 | 0.7330   |
| 0.2476        | 7.34  | 800  | 1.2026          | 0.6077    | 0.6858 | 0.6444 | 0.7287   |
| 0.2476        | 7.8   | 850  | 1.1666          | 0.6176    | 0.7678 | 0.6845 | 0.7482   |
| 0.2476        | 8.26  | 900  | 1.1741          | 0.6119    | 0.7842 | 0.6874 | 0.7518   |
| 0.2476        | 8.72  | 950  | 1.3172          | 0.5584    | 0.6667 | 0.6077 | 0.7214   |
| 0.0227        | 9.17  | 1000 | 1.3335          | 0.5868    | 0.7295 | 0.6504 | 0.7185   |
| 0.0227        | 9.63  | 1050 | 1.2987          | 0.6247    | 0.7459 | 0.6800 | 0.7352   |
| 0.0227        | 10.09 | 1100 | 1.4033          | 0.5391    | 0.5464 | 0.5427 | 0.7041   |
| 0.0227        | 10.55 | 1150 | 1.5544          | 0.5427    | 0.6940 | 0.6091 | 0.7113   |
| 0.0227        | 11.01 | 1200 | 1.5020          | 0.5771    | 0.5519 | 0.5642 | 0.7221   |
| 0.0227        | 11.47 | 1250 | 1.3234          | 0.5983    | 0.7486 | 0.6650 | 0.7381   |
| 0.0227        | 11.93 | 1300 | 1.4603          | 0.6197    | 0.7213 | 0.6667 | 0.7359   |
| 0.0227        | 12.39 | 1350 | 1.5133          | 0.5301    | 0.5301 | 0.5301 | 0.6975   |
| 0.0227        | 12.84 | 1400 | 1.4874          | 0.5671    | 0.7623 | 0.6503 | 0.7366   |
| 0.0227        | 13.3  | 1450 | 1.5313          | 0.5603    | 0.7240 | 0.6317 | 0.7279   |
| 0.0075        | 13.76 | 1500 | 1.4268          | 0.5895    | 0.6749 | 0.6293 | 0.7229   |
| 0.0075        | 14.22 | 1550 | 1.6733          | 0.5190    | 0.5219 | 0.5204 | 0.6939   |
| 0.0075        | 14.68 | 1600 | 1.5003          | 0.5749    | 0.7650 | 0.6565 | 0.7366   |
| 0.0075        | 15.14 | 1650 | 1.5747          | 0.6353    | 0.5902 | 0.6119 | 0.7294   |
| 0.0075        | 15.6  | 1700 | 1.4836          | 0.5484    | 0.5574 | 0.5528 | 0.7048   |
| 0.0075        | 16.06 | 1750 | 1.7085          | 0.5066    | 0.5273 | 0.5167 | 0.6932   |
| 0.0075        | 16.51 | 1800 | 1.6691          | 0.5669    | 0.5328 | 0.5493 | 0.7048   |
| 0.0075        | 16.97 | 1850 | 1.5524          | 0.534     | 0.7295 | 0.6166 | 0.7236   |
| 0.0075        | 17.43 | 1900 | 1.5616          | 0.5484    | 0.6038 | 0.5748 | 0.7156   |
| 0.0075        | 17.89 | 1950 | 1.5597          | 0.5622    | 0.6667 | 0.61   | 0.7192   |
| 0.0044        | 18.35 | 2000 | 1.4448          | 0.6106    | 0.7842 | 0.6866 | 0.7525   |
| 0.0044        | 18.81 | 2050 | 1.5741          | 0.5802    | 0.5137 | 0.5449 | 0.7055   |
| 0.0044        | 19.27 | 2100 | 1.6085          | 0.5842    | 0.6448 | 0.6130 | 0.7192   |
| 0.0044        | 19.72 | 2150 | 1.5787          | 0.6016    | 0.8087 | 0.6900 | 0.7547   |
| 0.0044        | 20.18 | 2200 | 1.6210          | 0.6004    | 0.8169 | 0.6921 | 0.7547   |
| 0.0044        | 20.64 | 2250 | 1.6739          | 0.5246    | 0.5246 | 0.5246 | 0.7026   |
| 0.0044        | 21.1  | 2300 | 1.7852          | 0.5618    | 0.5710 | 0.5664 | 0.6990   |
| 0.0044        | 21.56 | 2350 | 1.6344          | 0.5576    | 0.6612 | 0.605  | 0.7142   |
| 0.0044        | 22.02 | 2400 | 1.8115          | 0.5363    | 0.5847 | 0.5595 | 0.7033   |
| 0.0044        | 22.48 | 2450 | 1.8336          | 0.5294    | 0.6148 | 0.5689 | 0.6968   |
| 0.0034        | 22.94 | 2500 | 1.7901          | 0.5878    | 0.6038 | 0.5957 | 0.7048   |
| 0.0034        | 23.39 | 2550 | 1.7766          | 0.5615    | 0.6858 | 0.6175 | 0.7113   |
| 0.0034        | 23.85 | 2600 | 1.8159          | 0.5531    | 0.6831 | 0.6112 | 0.7084   |
| 0.0034        | 24.31 | 2650 | 1.8307          | 0.6075    | 0.6175 | 0.6125 | 0.7142   |
| 0.0034        | 24.77 | 2700 | 1.8326          | 0.5410    | 0.6667 | 0.5973 | 0.7055   |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "My name is Quy Dinh and I love Fried Chicken with curry sauce and then dessert with coconut panna cotta", "example_title": "Example 1: Random sentence"}, {"text": "and Scallop Teppanyaki, Steamed Egg Custard,", "example_title": "Example 2: Splitted from menu"}], "model-index": [{"name": "chope-fine-dishing-distilbert-base-uncased-finetuned-ner-v0.1", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

chope-fine-dishing-distilbert-base-uncased-finetuned-ner-v0.1
=============================================================


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


* Loss: 1.4448
* Precision: 0.6106
* Recall: 0.7842
* F1: 0.6866
* Accuracy: 0.7525


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: 50


### Training results



### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* 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: 50 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #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: 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: 50 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #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: 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: 50### Training results### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2