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# roberta-base-finetuned-mrpc

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2891
- Accuracy: 0.8925
- F1: 0.9228

## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- training precision: Mixed Precision

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5998        | 1.0   | 57   | 0.5425          | 0.74     | 0.8349 |
| 0.5058        | 2.0   | 114  | 0.3020          | 0.875    | 0.9084 |
| 0.3316        | 3.0   | 171  | 0.2891          | 0.8925   | 0.9228 |
| 0.1617        | 4.0   | 228  | 0.2937          | 0.8825   | 0.9138 |
| 0.3161        | 5.0   | 285  | 0.3193          | 0.8875   | 0.9171 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "roberta-base-finetuned-mrpc", "results": []}]}
transformers pytorch optimum_graphcore roberta text-classification generated_from_trainer dataset:glue license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #optimum_graphcore #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-finetuned-mrpc
===========================


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


* Loss: 0.2891
* Accuracy: 0.8925
* F1: 0.9228


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: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: IPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* training precision: Mixed Precision


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.0+cpu
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: IPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* training precision: Mixed Precision ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.0+cpu
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #optimum_graphcore #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: IPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* training precision: Mixed Precision ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.0+cpu
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #optimum_graphcore #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: IPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* training precision: Mixed Precision### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.0+cpu
* Datasets 2.3.2
* Tokenizers 0.12.1