
# bert_uncased_L-12_H-128_A-2-mlm-multi-emails-hq

This model is a fine-tuned version of [google/bert_uncased_L-12_H-128_A-2](https://huggingface.co/google/bert_uncased_L-12_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4235
- Accuracy: 0.5780

## Model description

This is a **40 MB version of BERT** that does surprisingly well!

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 8.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.9421        | 0.99  | 141  | 2.7769          | 0.5330   |
| 2.772         | 1.99  | 282  | 2.6669          | 0.5487   |
| 2.6997        | 2.99  | 423  | 2.5486          | 0.5621   |
| 2.6281        | 3.99  | 564  | 2.4865          | 0.5704   |
| 2.5626        | 4.99  | 705  | 2.4385          | 0.5766   |
| 2.5504        | 5.99  | 846  | 2.4421          | 0.5772   |
| 2.5434        | 6.99  | 987  | 2.4094          | 0.5818   |
| 2.5174        | 7.99  | 1128 | 2.4235          | 0.5780   |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 2.0.0.dev20230129+cu118
- Datasets 2.8.0
- Tokenizers 0.13.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "Can you please send me the [MASK] by the end of the day?", "example_title": "end of day"}, {"text": "I hope this email finds you well. I wanted to follow up on our [MASK] yesterday.", "example_title": "follow-up"}, {"text": "The meeting has been rescheduled to [MASK].", "example_title": "reschedule"}, {"text": "Please let me know if you need any further [MASK] regarding the project.", "example_title": "further help"}, {"text": "I appreciate your prompt response to my previous email. Can you provide an update on the [MASK] by tomorrow?", "example_title": "provide update"}, {"text": "Paris is the [MASK] of France.", "example_title": "paris (default)"}, {"text": "The goal of life is [MASK].", "example_title": "goal of life (default)"}], "model-index": [{"name": "bert_uncased_L-12_H-128_A-2-mlm-multi-emails-hq-r2", "results": []}]}
transformers pytorch bert fill-mask generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert\_uncased\_L-12\_H-128\_A-2-mlm-multi-emails-hq
===================================================


This model is a fine-tuned version of google/bert\_uncased\_L-12\_H-128\_A-2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.4235
* Accuracy: 0.5780


Model description
-----------------


This is a 40 MB version of BERT that does surprisingly well!


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: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 8.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 2.0.0.dev20230129+cu118
* Datasets 2.8.0
* Tokenizers 0.13.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 8.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 2.0.0.dev20230129+cu118
* Datasets 2.8.0
* Tokenizers 0.13.1
TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 8.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 2.0.0.dev20230129+cu118
* Datasets 2.8.0
* Tokenizers 0.13.1
TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 8.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 2.0.0.dev20230129+cu118
* Datasets 2.8.0
* Tokenizers 0.13.1