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# xlm-roberta-large-xnli-finetuned-mnli

This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2542
- Accuracy: 0.8549

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7468        | 1.0   | 2250  | 0.8551          | 0.8348   |
| 0.567         | 2.0   | 4500  | 0.8935          | 0.8377   |
| 0.318         | 3.0   | 6750  | 0.9892          | 0.8492   |
| 0.1146        | 4.0   | 9000  | 1.2373          | 0.8446   |
| 0.0383        | 5.0   | 11250 | 1.2542          | 0.8549   |


### Framework versions

- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "xlm-roberta-large-xnli-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mnli"}, "metrics": [{"type": "accuracy", "value": 0.8548888888888889, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer dataset:glue license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

xlm-roberta-large-xnli-finetuned-mnli
=====================================


This model is a fine-tuned version of joeddav/xlm-roberta-large-xnli on the glue dataset.
It achieves the following results on the evaluation set:


* Loss: 1.2542
* Accuracy: 0.8549


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 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.3.0
* Tokenizers 0.12.1



### 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 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.3.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #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 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.3.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #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 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.3.0
* Tokenizers 0.12.1