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# bart-large-mnli-aitools-8n-p

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: 0.3997
- Precision: 0.9375

## 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 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| No log        | 0.07  | 50   | 0.5082          | 0.0       |
| No log        | 0.14  | 100  | 0.5312          | 0.0       |
| No log        | 0.21  | 150  | 0.3020          | 0.7895    |
| No log        | 0.27  | 200  | 0.3802          | 0.8667    |
| No log        | 0.34  | 250  | 0.3721          | 0.9231    |
| No log        | 0.41  | 300  | 0.3692          | 0.8       |
| No log        | 0.48  | 350  | 0.4657          | 1.0       |
| No log        | 0.55  | 400  | 0.5192          | 0.75      |
| No log        | 0.62  | 450  | 0.4348          | 0.8667    |
| 0.3718        | 0.68  | 500  | 0.3369          | 0.8421    |
| 0.3718        | 0.75  | 550  | 0.3150          | 0.8889    |
| 0.3718        | 0.82  | 600  | 0.2712          | 1.0       |
| 0.3718        | 0.89  | 650  | 0.2900          | 0.85      |
| 0.3718        | 0.96  | 700  | 0.2895          | 0.9375    |
| 0.3718        | 1.03  | 750  | 0.2578          | 0.7826    |
| 0.3718        | 1.09  | 800  | 0.3731          | 0.9412    |
| 0.3718        | 1.16  | 850  | 0.1916          | 0.8571    |
| 0.3718        | 1.23  | 900  | 0.1980          | 0.85      |
| 0.3718        | 1.3   | 950  | 0.3446          | 0.9412    |
| 0.2003        | 1.37  | 1000 | 0.3997          | 0.9375    |
| 0.2003        | 1.44  | 1050 | 0.3500          | 0.85      |
| 0.2003        | 1.5   | 1100 | 0.2820          | 0.7917    |
| 0.2003        | 1.57  | 1150 | 0.3192          | 0.8947    |


### 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": ["precision"], "model-index": [{"name": "bart-large-mnli-aitools-8n-p", "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-8n-p
============================


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: 0.3997
* Precision: 0.9375


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