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# bert-base-uncased-finetuned-mnli-rte-wnli-10

This model is a fine-tuned version of [yy642/bert-base-uncased-finetuned-mnli-rte-wnli-5](https://huggingface.co/yy642/bert-base-uncased-finetuned-mnli-rte-wnli-5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5876
- Accuracy: 0.9206

## 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0641        | 1.0   | 16558 | 0.4528          | 0.9138   |
| 0.0479        | 2.0   | 33116 | 0.5116          | 0.9153   |
| 0.0363        | 3.0   | 49674 | 0.5660          | 0.9138   |
| 0.0244        | 4.0   | 66232 | 0.5876          | 0.9206   |
| 0.0145        | 5.0   | 82790 | 0.6156          | 0.9192   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-mnli-rte-wnli-10", "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-base-uncased-finetuned-mnli-rte-wnli-10
============================================


This model is a fine-tuned version of yy642/bert-base-uncased-finetuned-mnli-rte-wnli-5 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5876
* Accuracy: 0.9206


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: 5
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0a0+17540c5
* Datasets 2.0.0
* Tokenizers 0.11.6



### 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: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0a0+17540c5
* Datasets 2.0.0
* Tokenizers 0.11.6
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: 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: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0a0+17540c5
* Datasets 2.0.0
* Tokenizers 0.11.6
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: 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: 5
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0a0+17540c5
* Datasets 2.0.0
* Tokenizers 0.11.6