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---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-finetuned-wikitext103-mlm-multi-emails-hq-x2bs
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)
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-finetuned-wikitext103-mlm-multi-emails-hq-x2bs
This model is a fine-tuned version of [saghar/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-finetuned-wikitext103](https://huggingface.co./saghar/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-finetuned-wikitext103) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0371
- Accuracy: 0.6450
## Model description
- masked language model
- mini version of RoBERTa
- does support uppercase text
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 16.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.2947 | 1.0 | 308 | 3.0832 | 0.5122 |
| 2.8727 | 2.0 | 616 | 2.6722 | 0.5662 |
| 2.6339 | 3.0 | 924 | 2.4797 | 0.5878 |
| 2.5053 | 4.0 | 1232 | 2.3833 | 0.6025 |
| 2.4531 | 5.0 | 1540 | 2.3085 | 0.6106 |
| 2.2852 | 6.0 | 1848 | 2.2451 | 0.6175 |
| 2.228 | 7.0 | 2156 | 2.1937 | 0.6244 |
| 2.2013 | 8.0 | 2464 | 2.1446 | 0.6310 |
| 2.1463 | 9.0 | 2772 | 2.1062 | 0.6357 |
| 2.0882 | 10.0 | 3080 | 2.0847 | 0.6370 |
| 2.1669 | 11.0 | 3388 | 2.0687 | 0.6399 |
| 2.0983 | 12.0 | 3696 | 2.0629 | 0.6423 |
| 2.1215 | 13.0 | 4004 | 2.0259 | 0.6476 |
| 2.1255 | 14.0 | 4312 | 2.0378 | 0.6461 |
| 2.1751 | 15.0 | 4620 | 2.0257 | 0.6458 |
| 1.9516 | 16.0 | 4928 | 2.0371 | 0.6450 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 2.0.0.dev20230212+cu118
- Datasets 2.9.0
- Tokenizers 0.13.2
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