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# bart-large-mnli-aitools-7n

This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2440
- Accuracy: 0.9653

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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.08  | 50   | 0.3695          | 0.8819   |
| No log        | 0.15  | 100  | 0.5887          | 0.8819   |
| No log        | 0.23  | 150  | 0.4348          | 0.8819   |
| No log        | 0.31  | 200  | 0.5770          | 0.8819   |
| No log        | 0.38  | 250  | 0.3552          | 0.9306   |
| No log        | 0.46  | 300  | 0.2887          | 0.9306   |
| No log        | 0.54  | 350  | 0.3606          | 0.9444   |
| No log        | 0.62  | 400  | 0.3048          | 0.9444   |
| No log        | 0.69  | 450  | 0.3399          | 0.9028   |
| 0.4278        | 0.77  | 500  | 0.3600          | 0.9236   |
| 0.4278        | 0.85  | 550  | 0.3100          | 0.9375   |
| 0.4278        | 0.92  | 600  | 0.3624          | 0.9444   |
| 0.4278        | 1.0   | 650  | 0.3367          | 0.9444   |
| 0.4278        | 1.08  | 700  | 0.2593          | 0.9444   |
| 0.4278        | 1.15  | 750  | 0.3215          | 0.9236   |
| 0.4278        | 1.23  | 800  | 0.3484          | 0.9306   |
| 0.4278        | 1.31  | 850  | 0.3628          | 0.9167   |
| 0.4278        | 1.38  | 900  | 0.3267          | 0.9444   |
| 0.4278        | 1.46  | 950  | 0.3527          | 0.9375   |
| 0.2206        | 1.54  | 1000 | 0.3661          | 0.9306   |
| 0.2206        | 1.62  | 1050 | 0.2522          | 0.9514   |
| 0.2206        | 1.69  | 1100 | 0.3929          | 0.9167   |
| 0.2206        | 1.77  | 1150 | 0.2960          | 0.9306   |
| 0.2206        | 1.85  | 1200 | 0.2918          | 0.9444   |
| 0.2206        | 1.92  | 1250 | 0.2746          | 0.9514   |
| 0.2206        | 2.0   | 1300 | 0.2954          | 0.9583   |
| 0.2206        | 2.08  | 1350 | 0.2634          | 0.9375   |
| 0.2206        | 2.15  | 1400 | 0.3141          | 0.9514   |
| 0.2206        | 2.23  | 1450 | 0.2427          | 0.9514   |
| 0.1761        | 2.31  | 1500 | 0.2440          | 0.9653   |
| 0.1761        | 2.38  | 1550 | 0.2204          | 0.9653   |
| 0.1761        | 2.46  | 1600 | 0.2171          | 0.9653   |
| 0.1761        | 2.54  | 1650 | 0.2676          | 0.9583   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bart-large-mnli-aitools-7n", "results": []}]}
transformers pytorch tensorboard bart text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bart-large-mnli-aitools-7n
==========================


This model is a fine-tuned version of facebook/bart-large-mnli on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2440
* Accuracy: 0.9653


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


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bart #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bart #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2