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---
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-4_H-128_A-2
model-index:
- name: bert_uncased_L-4_H-128_A-2-mlm-multi-emails-hq
results: []
---
# 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
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