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# histbert-finetuned-ner

This model is a fine-tuned version of [Riksarkivet/bert-base-cased-swe-historical](https://huggingface.co/Riksarkivet/bert-base-cased-swe-historical) on the sucx3_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0495
- Precision: 0.8784
- Recall: 0.9261
- F1: 0.9017
- Accuracy: 0.9922

## 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: 7

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0403        | 1.0   | 5391  | 0.0316          | 0.8496    | 0.8866 | 0.8677 | 0.9903   |
| 0.0199        | 2.0   | 10782 | 0.0308          | 0.8814    | 0.9034 | 0.8923 | 0.9915   |
| 0.0173        | 3.0   | 16173 | 0.0372          | 0.8698    | 0.9197 | 0.8940 | 0.9913   |
| 0.0066        | 4.0   | 21564 | 0.0397          | 0.8783    | 0.9239 | 0.9005 | 0.9921   |
| 0.0029        | 5.0   | 26955 | 0.0454          | 0.8855    | 0.9181 | 0.9015 | 0.9923   |
| 0.0035        | 6.0   | 32346 | 0.0454          | 0.8834    | 0.9211 | 0.9019 | 0.9922   |
| 0.0009        | 7.0   | 37737 | 0.0495          | 0.8784    | 0.9261 | 0.9017 | 0.9922   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["sucx3_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "histbert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sucx3_ner", "type": "sucx3_ner", "config": "simple_cased", "split": "validation", "args": "simple_cased"}, "metrics": [{"type": "precision", "value": 0.8784308810627898, "name": "Precision"}, {"type": "recall", "value": 0.9261363636363636, "name": "Recall"}, {"type": "f1", "value": 0.9016530520357625, "name": "F1"}, {"type": "accuracy", "value": 0.992218705252845, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:sucx3_ner license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-sucx3_ner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

histbert-finetuned-ner
======================


This model is a fine-tuned version of Riksarkivet/bert-base-cased-swe-historical on the sucx3\_ner dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0495
* Precision: 0.8784
* Recall: 0.9261
* F1: 0.9017
* Accuracy: 0.9922


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: 7


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 7 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-sucx3_ner #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: 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: 7 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3