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# BERT-evidence-types

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the evidence types dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8008
- Macro f1: 0.4227
- Weighted f1: 0.6976
- Accuracy: 0.7154
- Balanced accuracy: 0.3876

## Training and evaluation data

The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository [BA-Thesis-Information-Science-Persuasion-Strategies](https://github.com/mariekevdh/BA-Thesis-Information-Science-Persuasion-Strategies)

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
| 1.1148        | 1.0   | 125  | 1.0531          | 0.2566   | 0.6570      | 0.6705   | 0.2753            |
| 0.7546        | 2.0   | 250  | 0.9725          | 0.3424   | 0.6947      | 0.7002   | 0.3334            |
| 0.4757        | 3.0   | 375  | 1.1375          | 0.3727   | 0.7113      | 0.7184   | 0.3680            |
| 0.2637        | 4.0   | 500  | 1.3585          | 0.3807   | 0.6836      | 0.6910   | 0.3805            |
| 0.1408        | 5.0   | 625  | 1.6605          | 0.3785   | 0.6765      | 0.6872   | 0.3635            |
| 0.0856        | 6.0   | 750  | 1.9703          | 0.3802   | 0.6890      | 0.7047   | 0.3704            |
| 0.0502        | 7.0   | 875  | 2.1245          | 0.4067   | 0.6995      | 0.7169   | 0.3751            |
| 0.0265        | 8.0   | 1000 | 2.2676          | 0.3756   | 0.6816      | 0.6925   | 0.3647            |
| 0.0147        | 9.0   | 1125 | 2.4286          | 0.4052   | 0.6887      | 0.7062   | 0.3803            |
| 0.0124        | 10.0  | 1250 | 2.5773          | 0.4084   | 0.6853      | 0.7040   | 0.3695            |
| 0.0111        | 11.0  | 1375 | 2.5941          | 0.4146   | 0.6915      | 0.7085   | 0.3834            |
| 0.0076        | 12.0  | 1500 | 2.6124          | 0.4157   | 0.6936      | 0.7078   | 0.3863            |
| 0.0067        | 13.0  | 1625 | 2.7050          | 0.4139   | 0.6925      | 0.7108   | 0.3798            |
| 0.0087        | 14.0  | 1750 | 2.6695          | 0.4252   | 0.7009      | 0.7169   | 0.3920            |
| 0.0056        | 15.0  | 1875 | 2.7357          | 0.4257   | 0.6985      | 0.7161   | 0.3868            |
| 0.0054        | 16.0  | 2000 | 2.7389          | 0.4249   | 0.6955      | 0.7116   | 0.3890            |
| 0.0051        | 17.0  | 2125 | 2.7767          | 0.4197   | 0.6967      | 0.7146   | 0.3863            |
| 0.004         | 18.0  | 2250 | 2.7947          | 0.4211   | 0.6977      | 0.7154   | 0.3876            |
| 0.0041        | 19.0  | 2375 | 2.8030          | 0.4204   | 0.6953      | 0.7131   | 0.3855            |
| 0.0042        | 20.0  | 2500 | 2.8008          | 0.4227   | 0.6976      | 0.7154   | 0.3876            |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "BERT-evidence-types", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

BERT-evidence-types
===================


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


* Loss: 2.8008
* Macro f1: 0.4227
* Weighted f1: 0.6976
* Accuracy: 0.7154
* Balanced accuracy: 0.3876


Training and evaluation data
----------------------------


The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository BA-Thesis-Information-Science-Persuasion-Strategies


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-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: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-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: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1