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# bert-for-patents-finetuned-ner

This model is a fine-tuned version of [anferico/bert-for-patents](https://huggingface.co/anferico/bert-for-patents) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2000
- Precision: 0.8850
- Recall: 0.9021
- F1: 0.8934
- Accuracy: 0.9606

## 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: cosine
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 23   | 1.0514          | 0.3450    | 0.4285 | 0.3822 | 0.7016   |
| No log        | 2.0   | 46   | 0.7917          | 0.4629    | 0.3607 | 0.4055 | 0.7519   |
| No log        | 3.0   | 69   | 0.6941          | 0.4830    | 0.6241 | 0.5446 | 0.7780   |
| No log        | 4.0   | 92   | 0.5767          | 0.5409    | 0.6947 | 0.6082 | 0.8128   |
| No log        | 5.0   | 115  | 0.4727          | 0.6292    | 0.7267 | 0.6745 | 0.8564   |
| No log        | 6.0   | 138  | 0.3939          | 0.7001    | 0.7587 | 0.7282 | 0.8854   |
| No log        | 7.0   | 161  | 0.3646          | 0.6818    | 0.8122 | 0.7413 | 0.8946   |
| No log        | 8.0   | 184  | 0.3300          | 0.7259    | 0.8184 | 0.7694 | 0.9076   |
| No log        | 9.0   | 207  | 0.2779          | 0.7944    | 0.8424 | 0.8177 | 0.9298   |
| No log        | 10.0  | 230  | 0.2541          | 0.8202    | 0.8610 | 0.8401 | 0.9398   |
| No log        | 11.0  | 253  | 0.2391          | 0.8509    | 0.8657 | 0.8582 | 0.9469   |
| No log        | 12.0  | 276  | 0.2340          | 0.8268    | 0.8790 | 0.8521 | 0.9442   |
| No log        | 13.0  | 299  | 0.2109          | 0.8707    | 0.8859 | 0.8782 | 0.9556   |
| No log        | 14.0  | 322  | 0.2032          | 0.8785    | 0.8971 | 0.8877 | 0.9576   |
| No log        | 15.0  | 345  | 0.2071          | 0.8700    | 0.8986 | 0.8841 | 0.9573   |
| No log        | 16.0  | 368  | 0.2005          | 0.8771    | 0.8989 | 0.8879 | 0.9585   |
| No log        | 17.0  | 391  | 0.2014          | 0.8855    | 0.8993 | 0.8923 | 0.9605   |
| No log        | 18.0  | 414  | 0.2008          | 0.8864    | 0.9024 | 0.8943 | 0.9606   |
| No log        | 19.0  | 437  | 0.2001          | 0.8847    | 0.9021 | 0.8933 | 0.9606   |
| No log        | 20.0  | 460  | 0.2000          | 0.8850    | 0.9021 | 0.8934 | 0.9606   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-for-patents-finetuned-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-for-patents-finetuned-ner
==============================


This model is a fine-tuned version of anferico/bert-for-patents on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2000
* Precision: 0.8850
* Recall: 0.9021
* F1: 0.8934
* Accuracy: 0.9606


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: cosine
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### 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: cosine
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3