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# textattack-roberta-base-MNLI-e-snli-classification-nli-base

This model is a fine-tuned version of [textattack/roberta-base-MNLI](https://huggingface.co/textattack/roberta-base-MNLI) on the esnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2488
- F1: 0.9106
- Accuracy: 0.9111

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 1.5376        | 0.05  | 400  | 0.4010          | 0.8556 | 0.8556   |
| 0.4352        | 0.09  | 800  | 0.3349          | 0.8795 | 0.8800   |
| 0.4           | 0.14  | 1200 | 0.3180          | 0.8851 | 0.8854   |
| 0.3801        | 0.19  | 1600 | 0.2975          | 0.8918 | 0.8921   |
| 0.3599        | 0.23  | 2000 | 0.2949          | 0.8951 | 0.8955   |
| 0.3612        | 0.28  | 2400 | 0.2802          | 0.8987 | 0.8987   |
| 0.3519        | 0.33  | 2800 | 0.2763          | 0.8977 | 0.8980   |
| 0.349         | 0.37  | 3200 | 0.2766          | 0.9020 | 0.9023   |
| 0.3432        | 0.42  | 3600 | 0.2748          | 0.9000 | 0.9001   |
| 0.3435        | 0.47  | 4000 | 0.2702          | 0.9051 | 0.9051   |
| 0.3352        | 0.51  | 4400 | 0.2728          | 0.9034 | 0.9039   |
| 0.3277        | 0.56  | 4800 | 0.2634          | 0.9039 | 0.9043   |
| 0.3307        | 0.61  | 5200 | 0.2623          | 0.9050 | 0.9057   |
| 0.3247        | 0.65  | 5600 | 0.2685          | 0.9059 | 0.9063   |
| 0.3175        | 0.7   | 6000 | 0.2589          | 0.9081 | 0.9084   |
| 0.3144        | 0.75  | 6400 | 0.2586          | 0.9088 | 0.9093   |
| 0.3102        | 0.79  | 6800 | 0.2547          | 0.9088 | 0.9090   |
| 0.3223        | 0.84  | 7200 | 0.2526          | 0.9093 | 0.9096   |
| 0.3166        | 0.89  | 7600 | 0.2490          | 0.9115 | 0.9118   |
| 0.3124        | 0.93  | 8000 | 0.2503          | 0.9106 | 0.9107   |
| 0.3053        | 0.98  | 8400 | 0.2452          | 0.9099 | 0.9101   |
| 0.2908        | 1.03  | 8800 | 0.2575          | 0.9113 | 0.9119   |
| 0.2853        | 1.07  | 9200 | 0.2464          | 0.9114 | 0.9118   |
| 0.2796        | 1.12  | 9600 | 0.2488          | 0.9106 | 0.9111   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.12.1+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "datasets": ["esnli"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "textattack-roberta-base-MNLI-e-snli-classification-nli-base", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "esnli", "type": "esnli", "config": "plain_text", "split": "validation", "args": "plain_text"}, "metrics": [{"type": "f1", "value": 0.9106202958294739, "name": "F1"}, {"type": "accuracy", "value": 0.9110953058321479, "name": "Accuracy"}]}]}]}
transformers pytorch roberta text-classification generated_from_trainer dataset:esnli model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-esnli #model-index #autotrain_compatible #endpoints_compatible #region-us 

textattack-roberta-base-MNLI-e-snli-classification-nli-base
===========================================================


This model is a fine-tuned version of textattack/roberta-base-MNLI on the esnli dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2488
* F1: 0.9106
* Accuracy: 0.9111


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: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 3
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-esnli #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-esnli #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 3
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2