初始化项目，由ModelHub XC社区提供模型
Model: postbot/gpt2-medium-emailgen Source: Original Platform
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README.md
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README.md
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---
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license:
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- apache-2.0
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tags:
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- text generation
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- emailgen
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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 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: 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: 0.3
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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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---
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# gpt2-medium-emailgen
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[](https://colab.research.google.com/gist/pszemraj/70058788c6d4b430398c12ee8ba10602/minimal-demo-for-postbot-gpt2-medium-emailgen.ipynb
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)
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Why write the entire email when you can generate (most of) it?
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```python
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from transformers import pipeline
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model_tag = "postbot/gpt2-medium-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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prompt = """
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Hello,
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Following up on the bubblegum shipment."""
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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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## about
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This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) 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: 1.5840
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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 is intended as a tool to save time writing predictable emails and not to write emails without a human-in-the-loop. validate that your email is factually correct before sending it to others.
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## Training and evaluation data
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- the dataset is essentially a hand-curated/augmented expansion to the classic `aeslc` dataset
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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: 0.001
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- distributed_type: multi-GPU
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- gradient_accumulation_steps: 8
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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: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 1.8701 | 1.0 | 789 | 1.8378 |
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| 1.5065 | 2.0 | 1578 | 1.6176 |
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| 1.1873 | 3.0 | 2367 | 1.5840 |
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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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- Datasets 2.5.1
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- Tokenizers 0.12.1
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_postbot__gpt2-medium-emailgen)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 25.97 |
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| ARC (25-shot) | 26.45 |
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| HellaSwag (10-shot) | 34.31 |
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| MMLU (5-shot) | 24.1 |
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| TruthfulQA (0-shot) | 43.96 |
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| Winogrande (5-shot) | 50.43 |
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| GSM8K (5-shot) | 0.0 |
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| DROP (3-shot) | 2.53 |
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