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