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# bert-finetuned-mrpc

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5152
- Accuracy: 0.8603
- F1: 0.9032
- Combined Score: 0.8818

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| No log        | 1.0   | 230  | 0.3668          | 0.8431   | 0.8881 | 0.8656         |
| No log        | 2.0   | 460  | 0.3751          | 0.8578   | 0.9017 | 0.8798         |
| 0.4264        | 3.0   | 690  | 0.5152          | 0.8603   | 0.9032 | 0.8818         |


### Framework versions

- Transformers 4.11.0.dev0
- Pytorch 1.8.1+cu111
- Datasets 1.10.3.dev0
- Tokenizers 0.10.3

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.8602941176470589, "name": "Accuracy"}, {"type": "f1", "value": 0.9032258064516129, "name": "F1"}]}, {"task": {"type": "natural-language-inference", "name": "Natural Language Inference"}, "dataset": {"name": "glue", "type": "glue", "config": "mrpc", "split": "validation"}, "metrics": [{"type": "accuracy", "value": 0.8602941176470589, "name": "Accuracy", "verified": true}, {"type": "precision", "value": 0.8580645161290322, "name": "Precision", "verified": true}, {"type": "recall", "value": 0.953405017921147, "name": "Recall", "verified": true}, {"type": "auc", "value": 0.9257731099441527, "name": "AUC", "verified": true}, {"type": "f1", "value": 0.9032258064516129, "name": "F1", "verified": true}, {"type": "loss", "value": 0.5150377154350281, "name": "loss", "verified": true}]}]}]}
transformers pytorch tensorboard safetensors bert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

bert-finetuned-mrpc
===================


This model is a fine-tuned version of bert-base-cased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5152
* Accuracy: 0.8603
* F1: 0.9032
* Combined Score: 0.8818


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 16
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0


### Training results



### Framework versions


* Transformers 4.11.0.dev0
* Pytorch 1.8.1+cu111
* Datasets 1.10.3.dev0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 16
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.11.0.dev0
* Pytorch 1.8.1+cu111
* Datasets 1.10.3.dev0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 16
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.11.0.dev0
* Pytorch 1.8.1+cu111
* Datasets 1.10.3.dev0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 16
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0### Training results### Framework versions


* Transformers 4.11.0.dev0
* Pytorch 1.8.1+cu111
* Datasets 1.10.3.dev0
* Tokenizers 0.10.3