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# bert-base-uncased-finetuned-scientific-eval

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1920
- Precision: 0.6240
- Recall: 0.7066
- F1: 0.6627
- Accuracy: 0.9502

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 71   | 0.3069          | 0.4263    | 0.3375 | 0.3768 | 0.9212   |
| No log        | 2.0   | 142  | 0.2241          | 0.4883    | 0.5899 | 0.5343 | 0.9359   |
| No log        | 3.0   | 213  | 0.1962          | 0.5745    | 0.6688 | 0.6181 | 0.9441   |
| No log        | 4.0   | 284  | 0.1920          | 0.6240    | 0.7066 | 0.6627 | 0.9502   |
| No log        | 5.0   | 355  | 0.2107          | 0.5694    | 0.7634 | 0.6523 | 0.9465   |
| No log        | 6.0   | 426  | 0.2070          | 0.6286    | 0.7634 | 0.6895 | 0.9514   |
| No log        | 7.0   | 497  | 0.2129          | 0.6193    | 0.7697 | 0.6864 | 0.9499   |
| 0.1579        | 8.0   | 568  | 0.2269          | 0.6496    | 0.7603 | 0.7006 | 0.9529   |
| 0.1579        | 9.0   | 639  | 0.2274          | 0.6366    | 0.7571 | 0.6916 | 0.9519   |
| 0.1579        | 10.0  | 710  | 0.2285          | 0.6486    | 0.7571 | 0.6987 | 0.9522   |


### Framework versions

- Transformers 4.27.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-scientific-eval", "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-uncased-finetuned-scientific-eval
===========================================


This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1920
* Precision: 0.6240
* Recall: 0.7066
* F1: 0.6627
* Accuracy: 0.9502


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: 10


### Training results



### Framework versions


* Transformers 4.27.2
* Pytorch 2.0.1+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: 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: 10 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* 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: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3