---
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>


    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>,


    I hope this email finds you well. I wanted to reach out and ask about office hours'
  example_title: office hours
- text: 'Grüße <NAME>,


    Ich hoffe, du hattest einen schönen Abend beim Wurstessen der Firma. Ich melde
    mich, weil'
  example_title: Wurstessen festival
- text: 'Guten Morgen Harold,


    ich habe mich gefragt, wann die nächste'
  example_title: event
- text: URGENT - I need the TPS reports
  example_title: URGENT
- text: 'Hoi Archibald,


    ik hoop dat deze e-mail je goed doet.'
  example_title: e-mails die je vinden
- text: 'Hello there.


    I just wanted to reach out and check in to'
  example_title: checking in
- text: 'Hello <NAME>,


    I 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>,


    I 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>,


    Właśnie 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
base_model: bigscience/bloom-1b1
---


# 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