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# roberta-large-e-snli-classification-nli-base

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the esnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2221
- F1: 0.9259
- Accuracy: 0.9260

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.9995        | 0.05  | 400  | 0.4236          | 0.8437 | 0.8465   |
| 0.4089        | 0.09  | 800  | 0.2961          | 0.8926 | 0.8933   |
| 0.3681        | 0.14  | 1200 | 0.2980          | 0.8914 | 0.8924   |
| 0.3467        | 0.19  | 1600 | 0.2872          | 0.8977 | 0.8990   |
| 0.324         | 0.23  | 2000 | 0.2506          | 0.9106 | 0.9110   |
| 0.3222        | 0.28  | 2400 | 0.2552          | 0.9132 | 0.9128   |
| 0.3138        | 0.33  | 2800 | 0.2379          | 0.9183 | 0.9183   |
| 0.3107        | 0.37  | 3200 | 0.2396          | 0.9152 | 0.9156   |
| 0.304         | 0.42  | 3600 | 0.2354          | 0.9174 | 0.9177   |
| 0.3027        | 0.47  | 4000 | 0.2360          | 0.9191 | 0.9191   |
| 0.2968        | 0.51  | 4400 | 0.2329          | 0.9182 | 0.9187   |
| 0.2888        | 0.56  | 4800 | 0.2462          | 0.9189 | 0.9196   |
| 0.2898        | 0.61  | 5200 | 0.2335          | 0.9206 | 0.9212   |
| 0.288         | 0.65  | 5600 | 0.2350          | 0.9220 | 0.9223   |
| 0.2746        | 0.7   | 6000 | 0.2208          | 0.9275 | 0.9278   |
| 0.2756        | 0.75  | 6400 | 0.2304          | 0.9209 | 0.9216   |
| 0.272         | 0.79  | 6800 | 0.2243          | 0.9237 | 0.9238   |
| 0.2809        | 0.84  | 7200 | 0.2176          | 0.9259 | 0.9261   |
| 0.2733        | 0.89  | 7600 | 0.2194          | 0.9271 | 0.9273   |
| 0.2723        | 0.93  | 8000 | 0.2221          | 0.9259 | 0.9260   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.12.1+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["esnli"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "roberta-large-e-snli-classification-nli-base", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "esnli", "type": "esnli", "config": "plain_text", "split": "validation", "args": "plain_text"}, "metrics": [{"type": "f1", "value": 0.9258678577111056, "name": "F1"}, {"type": "accuracy", "value": 0.9260312944523471, "name": "Accuracy"}]}]}]}
transformers pytorch roberta text-classification generated_from_trainer dataset:esnli license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-esnli #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

roberta-large-e-snli-classification-nli-base
============================================


This model is a fine-tuned version of roberta-large on the esnli dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2221
* F1: 0.9259
* Accuracy: 0.9260


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: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 3
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-esnli #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-esnli #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 3
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2