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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_uncased_L-12_H-128_A-2-mlm-multi-emails-hq-r2
results: []
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-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
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