---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_uncased_L-4_H-128_A-2-mlm-multi-emails-hq
  results: []
datasets:
  - postbot/multi-emails-hq
language:
  - en
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)
---


# bert_uncased_L-4_H-128_A-2-mlm-multi-emails-hq

This model is a fine-tuned version of [google/bert_uncased_L-4_H-128_A-2](https://huggingface.co/google/bert_uncased_L-4_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8524
- Accuracy: 0.5077

## Model description

Double the layers of BERT-tiny, fine-tuned on email data for eight epochs.

## Intended uses & limitations

- This is primarily an example/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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.5477        | 0.99  | 141  | 3.2637          | 0.4551   |
| 3.3307        | 1.99  | 282  | 3.0873          | 0.4785   |
| 3.252         | 2.99  | 423  | 2.9842          | 0.4911   |
| 3.1415        | 3.99  | 564  | 2.9230          | 0.4995   |
| 3.0903        | 4.99  | 705  | 2.8625          | 0.5070   |
| 3.0996        | 5.99  | 846  | 2.8615          | 0.5087   |
| 3.0641        | 6.99  | 987  | 2.8407          | 0.5120   |
| 3.0514        | 7.99  | 1128 | 2.8524          | 0.5077   |


### Framework versions

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