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  license: apache-2.0
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  tags:
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  - generated_from_trainer
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- model-index:
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- - name: distilgpt2-emailgen-V2-emailgen_DS-multi-clean-100k_Ep-4_Bs-16
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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- # distilgpt2-emailgen-V2-emailgen_DS-multi-clean-100k_Ep-4_Bs-16
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- This model is a fine-tuned version of [postbot/distilgpt2-emailgen-V2](https://huggingface.co/postbot/distilgpt2-emailgen-V2) on the None dataset.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  It achieves the following results on the evaluation set:
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  - Loss: 1.9126
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@@ -30,7 +93,11 @@ 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: 0.0006
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  license: apache-2.0
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  tags:
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  - generated_from_trainer
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+ - distilgpt2
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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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+
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+ widget:
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+ - text: "Good Morning Professor Beans,
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+
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+ Hope you are doing well. I just wanted to reach out and ask if differential calculus will be on the exam"
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+ example_title: "email to prof"
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+ - text: "Hey <NAME>,\n\nThank you for signing up for my weekly newsletter. Before we get started, you'll have to confirm your email address."
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+ example_title: "newsletter"
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+ - text: "Hi <NAME>,\n\nI 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>,\n\nI hope you had a splendid evening at the Company sausage eating festival. I am reaching out because"
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+ example_title: "festival"
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+ - text: "Good Morning Harold,\n\nI 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,\n\nI hope this email finds you extremely well."
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+ example_title: "emails that find you"
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+ - text: "Hello there.\n\nI just wanted to reach out and check in to"
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+ example_title: "checking in"
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+ - 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"
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+ example_title: "work well"
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+ - text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if we could catch 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 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: 4
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+ max_length: 128
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+ length_penalty: 0.8
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+ no_repeat_ngram_size: 2
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+ do_sample: False
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+ num_beams: 8
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+ early_stopping: True
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+ repetition_penalty: 5.5
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  ---
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+ # distilgpt2-emailgen: V2
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+ > This is a V2, which should perform better than V1. This is in the process of being evaluated.
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+
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+ Why write the rest of your email when you can generate it?
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+
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+ ```python
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+ from transformers import pipeline
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+
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+ model_tag = "postbot/distilgpt2-emailgen"
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+ generator = pipeline(
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+ 'text-generation',
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+ model=model_tag,
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+ )
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+
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+ prompt = """
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+ Hello,
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+
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+ Following up on the bubblegum shipment."""
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+
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+ result = generator(
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+ prompt,
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+ max_length=64,
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+ do_sample=False,
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+ early_stopping=True,
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+ ) # generate
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+ print(result[0]['generated_text'])
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+ ```
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+
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+ This model is a fine-tuned version of [postbot/distilgpt2-emailgen-V2](https://huggingface.co/postbot/distilgpt2-emailgen-V2) 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: 1.9126
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  ## Training procedure
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+ ### Training hyperparameters (run 1/2)
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+
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+ TODO
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+
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+ ### Training hyperparameters (run 2/2)
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0006