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# bert-base-NER-finetuned-ner

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the x_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4380
- Precision: 0.2274
- Recall: 0.1119
- F1: 0.1499
- Accuracy: 0.8485

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0822        | 1.0   | 878  | 1.1648          | 0.2068    | 0.1101 | 0.1437 | 0.8471   |
| 0.0102        | 2.0   | 1756 | 1.2697          | 0.2073    | 0.1110 | 0.1445 | 0.8447   |
| 0.0049        | 3.0   | 2634 | 1.3945          | 0.2006    | 0.1073 | 0.1399 | 0.8368   |
| 0.0025        | 4.0   | 3512 | 1.3994          | 0.2243    | 0.1126 | 0.1499 | 0.8501   |
| 0.0011        | 5.0   | 4390 | 1.4380          | 0.2274    | 0.1119 | 0.1499 | 0.8485   |


### Framework versions

- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["x_glue"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-NER-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "x_glue", "type": "x_glue", "args": "ner"}, "metrics": [{"type": "precision", "value": 0.2273838630806846, "name": "Precision"}, {"type": "recall", "value": 0.11185727172496743, "name": "Recall"}, {"type": "f1", "value": 0.14994961370507223, "name": "F1"}, {"type": "accuracy", "value": 0.8485324947589099, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:x_glue license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-x_glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-NER-finetuned-ner
===========================


This model is a fine-tuned version of dslim/bert-base-NER on the x\_glue dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4380
* Precision: 0.2274
* Recall: 0.1119
* F1: 0.1499
* Accuracy: 0.8485


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: 5


### Training results



### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3



### 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: 5 ### Training results ### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-x_glue #license-mit #model-index #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: 5 ### Training results ### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-x_glue #license-mit #model-index #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: 5### Training results### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3