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# sa_BERT_no_pretrain_mnli

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7747
- Accuracy: 0.6701

## 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: 4e-05
- train_batch_size: 96
- eval_batch_size: 96
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9765        | 1.0   | 4091  | 0.9090          | 0.5823   |
| 0.8799        | 2.0   | 8182  | 0.8625          | 0.6123   |
| 0.8193        | 3.0   | 12273 | 0.8227          | 0.6362   |
| 0.7551        | 4.0   | 16364 | 0.7929          | 0.6542   |
| 0.6961        | 5.0   | 20455 | 0.7901          | 0.6643   |
| 0.6403        | 6.0   | 24546 | 0.8298          | 0.6687   |
| 0.5831        | 7.0   | 28637 | 0.8135          | 0.6701   |
| 0.5224        | 8.0   | 32728 | 0.8831          | 0.6718   |
| 0.4602        | 9.0   | 36819 | 0.9055          | 0.6652   |
| 0.4003        | 10.0  | 40910 | 0.9812          | 0.6603   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "sa_BERT_no_pretrain_mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "config": "mnli", "split": "validation_matched", "args": "mnli"}, "metrics": [{"type": "accuracy", "value": 0.6700569568755086, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

sa\_BERT\_no\_pretrain\_mnli
============================


This model is a fine-tuned version of bert-base-uncased on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7747
* Accuracy: 0.6701


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: 4e-05
* train\_batch\_size: 96
* eval\_batch\_size: 96
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.30.2
* Pytorch 1.14.0a0+410ce96
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 96
* eval\_batch\_size: 96
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.30.2
* Pytorch 1.14.0a0+410ce96
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 96
* eval\_batch\_size: 96
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.30.2
* Pytorch 1.14.0a0+410ce96
* Datasets 2.12.0
* Tokenizers 0.13.3