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# ner_nerd_fine

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the nerd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3373
- Precision: 0.6326
- Recall: 0.6734
- F1: 0.6524
- Accuracy: 0.9050

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6219        | 1.0   | 8235  | 0.3347          | 0.6066    | 0.6581 | 0.6313 | 0.9015   |
| 0.3071        | 2.0   | 16470 | 0.3165          | 0.6349    | 0.6637 | 0.6490 | 0.9060   |
| 0.2384        | 3.0   | 24705 | 0.3311          | 0.6373    | 0.6769 | 0.6565 | 0.9068   |
| 0.1834        | 4.0   | 32940 | 0.3414          | 0.6349    | 0.6780 | 0.6557 | 0.9069   |
| 0.1392        | 5.0   | 41175 | 0.3793          | 0.6334    | 0.6775 | 0.6547 | 0.9068   |


### Framework versions

- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["nerd"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "ner_nerd_fine", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "nerd", "type": "nerd", "args": "nerd"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9050232835369201}}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:nerd license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-nerd #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

ner\_nerd\_fine
===============


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


* Loss: 0.3373
* Precision: 0.6326
* Recall: 0.6734
* F1: 0.6524
* Accuracy: 0.9050


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: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.9.1
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.9.1
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-nerd #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.9.1
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.2
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-nerd #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.9.1
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.2