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# albert-base-v2_mnli_bc

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2952
- Accuracy: 0.9399

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2159        | 1.0   | 16363 | 0.2268          | 0.9248   |
| 0.1817        | 2.0   | 32726 | 0.2335          | 0.9347   |
| 0.0863        | 3.0   | 49089 | 0.3014          | 0.9401   |


### Framework versions

- Transformers 4.13.0
- Pytorch 1.10.1+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2_mnli_bc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "args": "mnli"}, "metrics": [{"type": "accuracy", "value": 0.9398776667163956, "name": "Accuracy"}]}]}]}
transformers pytorch albert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

albert-base-v2\_mnli\_bc
========================


This model is a fine-tuned version of albert-base-v2 on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2952
* Accuracy: 0.9399


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: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.13.0
* Pytorch 1.10.1+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.13.0
* Pytorch 1.10.1+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #albert #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: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.13.0
* Pytorch 1.10.1+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #albert #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: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.13.0
* Pytorch 1.10.1+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3