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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- postbot/multi-emails-hq |
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metrics: |
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- accuracy |
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pipeline_tag: fill-mask |
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widget: |
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- text: Can you please send me the [MASK] by the end of the day? |
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example_title: end of day |
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- text: I hope this email finds you well. I wanted to follow up on our [MASK] yesterday. |
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example_title: follow-up |
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- text: The meeting has been rescheduled to [MASK]. |
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example_title: reschedule |
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- text: Please let me know if you need any further [MASK] regarding the project. |
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example_title: further help |
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- text: I appreciate your prompt response to my previous email. Can you provide an |
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update on the [MASK] by tomorrow? |
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example_title: provide update |
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- text: Paris is the [MASK] of France. |
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example_title: paris (default) |
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- text: The goal of life is [MASK]. |
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example_title: goal of life (default) |
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base_model: google/bert_uncased_L-2_H-128_A-2 |
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model-index: |
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- name: bert_uncased_L-2_H-128_A-2-mlm-multi-emails-hq |
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results: [] |
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--- |
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# bert_uncased_L-2_H-128_A-2-mlm-multi-emails-hq (BERT-tiny) |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.0981 |
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- Accuracy: 0.4728 |
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## Model description |
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BERT-tiny fine-tuned on email data for eight epochs. |
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## Intended uses & limitations |
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- this is mostly a test |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 8 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 8.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 3.8974 | 0.99 | 141 | 3.5129 | 0.4218 | |
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| 3.7009 | 1.99 | 282 | 3.3295 | 0.4452 | |
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| 3.5845 | 2.99 | 423 | 3.2219 | 0.4589 | |
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| 3.4976 | 3.99 | 564 | 3.1618 | 0.4666 | |
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| 3.4356 | 4.99 | 705 | 3.1002 | 0.4739 | |
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| 3.4493 | 5.99 | 846 | 3.1028 | 0.4746 | |
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| 3.4199 | 6.99 | 987 | 3.0857 | 0.4766 | |
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| 3.4086 | 7.99 | 1128 | 3.0981 | 0.4728 | |
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### Framework versions |
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- Transformers 4.27.0.dev0 |
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- Pytorch 2.0.0.dev20230129+cu118 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.1 |
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