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# distilbert-base-uncased-mapa-ner-coarse_grained-v2

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: 0.1627
- Precision: 0.7898
- Recall: 0.4843
- F1: 0.6004
- Accuracy: 0.9857

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0354        | 1.0   | 1739  | 0.0834          | 0.7023    | 0.4612 | 0.5568 | 0.9840   |
| 0.0255        | 2.0   | 3478  | 0.1034          | 0.8172    | 0.4355 | 0.5682 | 0.9852   |
| 0.0168        | 3.0   | 5217  | 0.0969          | 0.7714    | 0.4588 | 0.5754 | 0.9848   |
| 0.0132        | 4.0   | 6956  | 0.1042          | 0.7477    | 0.4838 | 0.5875 | 0.9852   |
| 0.0112        | 5.0   | 8695  | 0.1109          | 0.7421    | 0.4863 | 0.5876 | 0.9849   |
| 0.0085        | 6.0   | 10434 | 0.1076          | 0.7194    | 0.4951 | 0.5865 | 0.9850   |
| 0.0067        | 7.0   | 12173 | 0.1343          | 0.7828    | 0.4587 | 0.5784 | 0.9849   |
| 0.0047        | 8.0   | 13912 | 0.1252          | 0.7425    | 0.4840 | 0.5860 | 0.9853   |
| 0.0045        | 9.0   | 15651 | 0.1410          | 0.7943    | 0.4615 | 0.5838 | 0.9852   |
| 0.0035        | 10.0  | 17390 | 0.1311          | 0.7624    | 0.4929 | 0.5987 | 0.9857   |
| 0.003         | 11.0  | 19129 | 0.1494          | 0.8059    | 0.4691 | 0.5930 | 0.9855   |
| 0.0025        | 12.0  | 20868 | 0.1436          | 0.7674    | 0.4852 | 0.5945 | 0.9856   |
| 0.002         | 13.0  | 22607 | 0.1513          | 0.7778    | 0.4741 | 0.5891 | 0.9852   |
| 0.0014        | 14.0  | 24346 | 0.1577          | 0.7986    | 0.4726 | 0.5938 | 0.9855   |
| 0.0016        | 15.0  | 26085 | 0.1573          | 0.7802    | 0.4766 | 0.5917 | 0.9855   |
| 0.0011        | 16.0  | 27824 | 0.1599          | 0.7917    | 0.4723 | 0.5916 | 0.9856   |
| 0.0012        | 17.0  | 29563 | 0.1601          | 0.7848    | 0.4867 | 0.6008 | 0.9857   |
| 0.001         | 18.0  | 31302 | 0.1572          | 0.7614    | 0.4939 | 0.5991 | 0.9856   |
| 0.0011        | 19.0  | 33041 | 0.1602          | 0.7858    | 0.4870 | 0.6013 | 0.9857   |
| 0.0009        | 20.0  | 34780 | 0.1627          | 0.7898    | 0.4843 | 0.6004 | 0.9857   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-mapa-ner-coarse_grained-v2", "results": []}]}
transformers pytorch distilbert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-mapa-ner-coarse\_grained-v2
===================================================


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: 0.1627
* Precision: 0.7898
* Recall: 0.4843
* F1: 0.6004
* Accuracy: 0.9857


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 #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: 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 #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: 20### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2