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---
license: bigscience-bloom-rail-1.0
tags:
- text generation
- generated_from_trainer
- email generation
- email
- emailgen
datasets:
- aeslc
- postbot/multi-emails-100k
widget:
- text: "Good Morning Professor Beans,
Hope you are doing well. I just wanted to reach out and ask if differential calculus will be on the exam"
example_title: "email to prof"
- text: "嘿<NAME>\n\n感谢你注册我的每周通讯。在我们开始之前，你必须确认你的电子邮件地址。."
example_title: "通讯"
- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and ask about office hours"
example_title: "office hours"
- text: "Grüße <NAME>,\n\nIch hoffe, du hattest einen schönen Abend beim Wurstessen der Firma. Ich melde mich, weil"
example_title: "Wurstessen festival"
- text: "Guten Morgen Harold,\n\nich habe mich gefragt, wann die nächste"
example_title: "event"
- text: "URGENT - I need the TPS reports"
example_title: "URGENT"
- text: "Hoi Archibald,\n\nik hoop dat deze e-mail je goed doet."
example_title: "e-mails die je vinden"
- text: "Hello there.\n\nI just wanted to reach out and check in to"
example_title: "checking in"
- text: "Hello <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if you've enjoyed your time with us"
example_title: "work well"
- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if we could catch up"
example_title: "catch up"
- text: "Jestem <NAME>,\n\nWłaśnie wprowadziłem się do obszaru i chciałem dotrzeć i uzyskać kilka szczegółów na temat tego, gdzie mogę dostać artykuły spożywcze i"
example_title: "zakupy spożywcze"
parameters:
min_length: 32
max_length: 128
no_repeat_ngram_size: 2
do_sample: True
temperature: 0.2
top_k: 20
top_p: 0.95
repetition_penalty: 3.5
length_penalty: 0.9
---
# bloom-1b1-emailgen - v1
This model is a fine-tuned version of [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1) on the ` postbot/multi-emails-100k` dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7397
## Model description
More information needed
## Intended uses & limitations
- **this model did not have any of the original layers frozen during training**
- while this is still an area of investigation, the model likely needs to have some layers frozen during fine-tuning to retain the multilingual capabilities in balance with learning how to write emails.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- 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: 0.03
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8465 | 1.0 | 256 | 1.8656 |
| 1.4903 | 2.0 | 512 | 1.7396 |
### details
```md
***** eval metrics *****
epoch = 2.0
eval_loss = 1.7397
eval_runtime = 0:04:27.41
eval_samples = 4216
eval_samples_per_second = 15.766
eval_steps_per_second = 15.766
perplexity = 5.6956
```
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
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