File size: 2,731 Bytes
ed83180 5e6dc0a ed83180 5e6dc0a ed83180 5e6dc0a ed83180 5e6dc0a ed83180 5e6dc0a ed83180 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_uncased_L-2_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-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
|