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

This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the swiss_judgment_prediction dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3456
- Accuracy: 0.7957

## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 5    | 1.8460          | 0.7956   |
| No log        | 2.0   | 10   | 1.3456          | 0.7957   |
| No log        | 3.0   | 15   | 1.2799          | 0.7957   |
| No log        | 4.0   | 20   | 1.2866          | 0.7957   |
| No log        | 5.0   | 25   | 1.3162          | 0.7956   |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["swiss_judgment_prediction"], "metrics": ["accuracy"], "model-index": [{"name": "xlm-roberta-large-xnli-finetuned-mnli-SJP", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "swiss_judgment_prediction", "type": "swiss_judgment_prediction", "args": "all_languages"}, "metrics": [{"type": "accuracy", "value": 0.7957142857142857, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer dataset:swiss_judgment_prediction license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-swiss_judgment_prediction #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

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


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


* Loss: 1.3456
* Accuracy: 0.7957


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: 2
* eval\_batch\_size: 2
* 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 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* 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 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-swiss_judgment_prediction #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: 2
* eval\_batch\_size: 2
* 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 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-swiss_judgment_prediction #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: 2
* eval\_batch\_size: 2
* 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 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1