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# jobbert-base-cased-ner

This model is a fine-tuned version of [jjzha/jobbert-base-cased](https://huggingface.co/jjzha/jobbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3789
- Job Title precision: 0.7916
- Job Title recall: 0.8721
- Job Title f1: 0.8299
- Loc precision: 0.8572
- Loc recall: 0.9506
- Loc f1: 0.9015
- Org precision: 0.6727
- Org recall: 0.7458
- Org f1: 0.7074
- Misc precision: 0.6893
- Misc recall: 0.6587
- Misc f1: 0.6736
- Precision: 0.7772
- Recall: 0.8551
- F1: 0.8143
- Accuracy: 0.8680

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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Job Title precision | Job Title recall | Job Title f1 | Loc precision | Loc recall | Loc f1 | Org precision | Org recall | Org f1 | Misc precision | Misc recall | Misc f1 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 308  | 0.3896          | 0.7827              | 0.8394           | 0.8101       | 0.8813        | 0.8856     | 0.8835 | 0.6865        | 0.7151     | 0.7005 | 0.6730         | 0.6041      | 0.6367  | 0.7800    | 0.8148 | 0.7970 | 0.8577   |
| 0.4672        | 2.0   | 616  | 0.3789          | 0.7916              | 0.8721           | 0.8299       | 0.8572        | 0.9506     | 0.9015 | 0.6727        | 0.7458     | 0.7074 | 0.6893         | 0.6587      | 0.6736  | 0.7772    | 0.8551 | 0.8143 | 0.8680   |
| 0.4672        | 3.0   | 924  | 0.4067          | 0.7800              | 0.8876           | 0.8304       | 0.8560        | 0.9443     | 0.8980 | 0.6928        | 0.7026     | 0.6977 | 0.6006         | 0.7440      | 0.6646  | 0.7730    | 0.8549 | 0.8119 | 0.8651   |


### Framework versions

- Transformers 4.28.1
- Pytorch 1.7.1+cu110
- Datasets 2.12.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "jobbert-base-cased-ner", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

jobbert-base-cased-ner
======================


This model is a fine-tuned version of jjzha/jobbert-base-cased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3789
* Job Title precision: 0.7916
* Job Title recall: 0.8721
* Job Title f1: 0.8299
* Loc precision: 0.8572
* Loc recall: 0.9506
* Loc f1: 0.9015
* Org precision: 0.6727
* Org recall: 0.7458
* Org f1: 0.7074
* Misc precision: 0.6893
* Misc recall: 0.6587
* Misc f1: 0.6736
* Precision: 0.7772
* Recall: 0.8551
* F1: 0.8143
* Accuracy: 0.8680


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


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 1.7.1+cu110
* Datasets 2.12.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 1.7.1+cu110
* Datasets 2.12.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 1.7.1+cu110
* Datasets 2.12.0
* Tokenizers 0.13.2