| --- |
| 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: [] |
| --- |
| |
|
|
| # 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 |
| |
