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# bert-base-uncased-finetuned-math_punctuation-25-01-two_linear_layers-frozen_bert

This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2150
- Micro f1: 0.8910
- Macro f1: 0.2672
- Weighted f1: 0.8495

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Micro f1 | Macro f1 | Weighted f1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
| 0.193         | 0.62  | 500  | 0.2146          | 0.8937   | 0.2360   | 0.8435      |
| 0.1936        | 1.23  | 1000 | 0.2130          | 0.8937   | 0.2360   | 0.8435      |
| 0.1924        | 1.85  | 1500 | 0.2119          | 0.8937   | 0.2361   | 0.8435      |
| 0.1911        | 2.47  | 2000 | 0.2128          | 0.8936   | 0.2369   | 0.8437      |
| 0.1909        | 3.09  | 2500 | 0.2114          | 0.8937   | 0.2369   | 0.8437      |
| 0.1904        | 3.7   | 3000 | 0.2137          | 0.8935   | 0.2407   | 0.8445      |
| 0.1935        | 4.32  | 3500 | 0.2138          | 0.8934   | 0.2469   | 0.8458      |
| 0.1874        | 4.94  | 4000 | 0.2118          | 0.8929   | 0.2561   | 0.8479      |
| 0.1908        | 5.56  | 4500 | 0.2134          | 0.8925   | 0.2588   | 0.8483      |
| 0.1877        | 6.17  | 5000 | 0.2135          | 0.8918   | 0.2628   | 0.8490      |
| 0.1881        | 6.79  | 5500 | 0.2133          | 0.8931   | 0.2554   | 0.8478      |
| 0.1902        | 7.41  | 6000 | 0.2137          | 0.8922   | 0.2603   | 0.8485      |
| 0.1883        | 8.02  | 6500 | 0.2155          | 0.8914   | 0.2655   | 0.8493      |
| 0.19          | 8.64  | 7000 | 0.2154          | 0.8914   | 0.2647   | 0.8490      |
| 0.1881        | 9.26  | 7500 | 0.2149          | 0.8915   | 0.2645   | 0.8492      |
| 0.1876        | 9.88  | 8000 | 0.2141          | 0.8911   | 0.2671   | 0.8496      |
| 0.1879        | 10.49 | 8500 | 0.2155          | 0.8897   | 0.2722   | 0.8501      |
| 0.1897        | 11.11 | 9000 | 0.2156          | 0.8910   | 0.2670   | 0.8494      |
| 0.1883        | 11.73 | 9500 | 0.2150          | 0.8910   | 0.2672   | 0.8495      |


### Framework versions

- Transformers 4.25.1
- Pytorch 2.0.0.dev20230111
- Datasets 2.8.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-math_punctuation-25-01-two_linear_layers-frozen_bert", "results": []}]}
transformers pytorch tensorboard generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #generated_from_trainer #endpoints_compatible #region-us 

bert-base-uncased-finetuned-math\_punctuation-25-01-two\_linear\_layers-frozen\_bert
====================================================================================


This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2150
* Micro f1: 0.8910
* Macro f1: 0.2672
* Weighted f1: 0.8495


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 2.0.0.dev20230111
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 2.0.0.dev20230111
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 2.0.0.dev20230111
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #generated_from_trainer #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 2.0.0.dev20230111
* Datasets 2.8.0
* Tokenizers 0.13.2