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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- text generation |
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- email generation |
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- email |
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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: 'Hey <NAME>, |
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Thank you for signing up for my weekly newsletter. Before we get started, you''ll |
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have to confirm your email address.' |
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example_title: newsletter |
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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: 'Greetings <NAME>, |
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I hope you had a splendid evening at the Company sausage eating festival. I am |
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reaching out because' |
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example_title: festival |
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- text: 'Good Morning Harold, |
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I was wondering when the next' |
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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: 'Hi Archibald, |
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I hope this email finds you extremely well.' |
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example_title: emails that find you |
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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: I'm <NAME> and I just moved into the area and wanted to reach out and get |
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some details on where I could get groceries and |
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example_title: grocery |
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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: 0.4 |
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top_k: 30 |
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top_p: 0.9 |
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repetition_penalty: 3.5 |
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length_penalty: 0.9 |
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base_model: EleutherAI/gpt-neo-1.3B |
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--- |
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# gpt-neo-1.3B-emailgen |
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This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) 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: 1.6930 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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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: 0.0001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 32 |
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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: 0.02 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.8669 | 1.0 | 789 | 1.7866 | |
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| 1.4049 | 2.0 | 1578 | 1.6930 | |
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### Framework versions |
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- Transformers 4.22.2 |
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- Pytorch 1.10.0+cu113 |
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- Tokenizers 0.12.1 |
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