
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-base-cased-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3793
- Job Title precision: 0.8079
- Job Title recall: 0.8248
- Job Title f1: 0.8163
- Loc precision: 0.8911
- Loc recall: 0.9081
- Loc f1: 0.8995
- Org precision: 0.6484
- Org recall: 0.7620
- Org f1: 0.7006
- Misc precision: 0.6134
- Misc recall: 0.7201
- Misc f1: 0.6625
- Precision: 0.7800
- Recall: 0.8265
- F1: 0.8025
- Accuracy: 0.8606

## 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  | 0.3793          | 0.8079              | 0.8248           | 0.8163       | 0.8911        | 0.9081     | 0.8995 | 0.6484        | 0.7620     | 0.7006 | 0.6134         | 0.7201      | 0.6625  | 0.7800    | 0.8265 | 0.8025 | 0.8606   |
| 0.4249        | 2.0   | 616  | 0.3866          | 0.7911              | 0.8728           | 0.8299       | 0.8676        | 0.9541     | 0.9088 | 0.6551        | 0.7886     | 0.7157 | 0.6623         | 0.6962      | 0.6789  | 0.7719    | 0.8669 | 0.8167 | 0.8685   |


### Framework versions

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

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

bert-base-cased-ner
===================


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


* Loss: 0.3793
* Job Title precision: 0.8079
* Job Title recall: 0.8248
* Job Title f1: 0.8163
* Loc precision: 0.8911
* Loc recall: 0.9081
* Loc f1: 0.8995
* Org precision: 0.6484
* Org recall: 0.7620
* Org f1: 0.7006
* Misc precision: 0.6134
* Misc recall: 0.7201
* Misc f1: 0.6625
* Precision: 0.7800
* Recall: 0.8265
* F1: 0.8025
* Accuracy: 0.8606


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 #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: 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