
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# roberta-base-mnli_CollSgE

This model is a fine-tuned version of [WillHeld/roberta-base-mnli](https://huggingface.co/WillHeld/roberta-base-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7610
- Acc: 0.8445

## 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: 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: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Acc    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4123        | 0.17  | 2000  | 0.4693          | 0.8332 |
| 0.4028        | 0.33  | 4000  | 0.4624          | 0.8338 |
| 0.3888        | 0.5   | 6000  | 0.4500          | 0.8375 |
| 0.3841        | 0.67  | 8000  | 0.4281          | 0.8416 |
| 0.3783        | 0.83  | 10000 | 0.4434          | 0.8365 |
| 0.3759        | 1.0   | 12000 | 0.4400          | 0.8418 |
| 0.2721        | 1.17  | 14000 | 0.5022          | 0.8427 |
| 0.2736        | 1.33  | 16000 | 0.5252          | 0.8431 |
| 0.2821        | 1.5   | 18000 | 0.4887          | 0.8409 |
| 0.2802        | 1.67  | 20000 | 0.4758          | 0.8458 |
| 0.2794        | 1.83  | 22000 | 0.4611          | 0.8458 |
| 0.2797        | 2.0   | 24000 | 0.4936          | 0.8456 |
| 0.1915        | 2.17  | 26000 | 0.5545          | 0.8462 |
| 0.1946        | 2.33  | 28000 | 0.5731          | 0.8443 |
| 0.2007        | 2.5   | 30000 | 0.5507          | 0.8428 |
| 0.2008        | 2.67  | 32000 | 0.5499          | 0.8454 |
| 0.1971        | 2.84  | 34000 | 0.5274          | 0.8483 |
| 0.2054        | 3.0   | 36000 | 0.5454          | 0.8476 |
| 0.1436        | 3.17  | 38000 | 0.6787          | 0.8442 |
| 0.1426        | 3.34  | 40000 | 0.6933          | 0.8421 |
| 0.1463        | 3.5   | 42000 | 0.6547          | 0.8455 |
| 0.1447        | 3.67  | 44000 | 0.6469          | 0.8438 |
| 0.1445        | 3.84  | 46000 | 0.6626          | 0.8472 |
| 0.1457        | 4.0   | 48000 | 0.6494          | 0.8504 |
| 0.1133        | 4.17  | 50000 | 0.7664          | 0.8459 |
| 0.1138        | 4.34  | 52000 | 0.7857          | 0.8452 |
| 0.1154        | 4.5   | 54000 | 0.7623          | 0.8486 |
| 0.1102        | 4.67  | 56000 | 0.7740          | 0.8460 |
| 0.1143        | 4.84  | 58000 | 0.7610          | 0.8445 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-mnli_CollSgE", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-mnli\_CollSgE
==========================


This model is a fine-tuned version of WillHeld/roberta-base-mnli on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7610
* Acc: 0.8445


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: 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: 5


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 5 ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #roberta #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: 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: 5 ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #roberta #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: 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: 5### Training results### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.12.1