

# bert_uncased_L-2_H-128_A-2-mlm-multi-emails-hq (BERT-tiny)

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0981
- Accuracy: 0.4728

## Model description

BERT-tiny fine-tuned on email data for eight epochs.


## Intended uses & limitations

- this is mostly a test 
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.8974        | 0.99  | 141  | 3.5129          | 0.4218   |
| 3.7009        | 1.99  | 282  | 3.3295          | 0.4452   |
| 3.5845        | 2.99  | 423  | 3.2219          | 0.4589   |
| 3.4976        | 3.99  | 564  | 3.1618          | 0.4666   |
| 3.4356        | 4.99  | 705  | 3.1002          | 0.4739   |
| 3.4493        | 5.99  | 846  | 3.1028          | 0.4746   |
| 3.4199        | 6.99  | 987  | 3.0857          | 0.4766   |
| 3.4086        | 7.99  | 1128 | 3.0981          | 0.4728   |


### Framework versions

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

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["postbot/multi-emails-hq"], "metrics": ["accuracy"], "pipeline_tag": "fill-mask", "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)"}], "base_model": "google/bert_uncased_L-2_H-128_A-2", "model-index": [{"name": "bert_uncased_L-2_H-128_A-2-mlm-multi-emails-hq", "results": []}]}
transformers pytorch safetensors bert fill-mask generated_from_trainer en dataset:postbot/multi-emails-hq base_model:google/bert_uncased_L-2_H-128_A-2 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #generated_from_trainer #en #dataset-postbot/multi-emails-hq #base_model-google/bert_uncased_L-2_H-128_A-2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert\_uncased\_L-2\_H-128\_A-2-mlm-multi-emails-hq (BERT-tiny)
==============================================================


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


* Loss: 3.0981
* Accuracy: 0.4728


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


BERT-tiny fine-tuned on email data for eight epochs.


Intended uses & limitations
---------------------------


* this is mostly a test


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 #safetensors #bert #fill-mask #generated_from_trainer #en #dataset-postbot/multi-emails-hq #base_model-google/bert_uncased_L-2_H-128_A-2 #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 #safetensors #bert #fill-mask #generated_from_trainer #en #dataset-postbot/multi-emails-hq #base_model-google/bert_uncased_L-2_H-128_A-2 #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