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distilgpt2-emailgen-V2-GGUF

GGUF
Generated from Trainer
distilgpt2
email generation
email
Inference Endpoints
Model card Files Community
distilgpt2-emailgen-V2-GGUF
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---
license: apache-2.0
tags:
- generated_from_trainer
- distilgpt2
- email generation
- email
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: "Hey <NAME>,\n\nThank you for signing up for my weekly newsletter. Before we get started, you'll have to confirm your email address." 
  example_title: "newsletter"
- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and ask about office hours" 
  example_title: "office hours"
- text: "Greetings <NAME>,\n\nI hope you had a splendid evening at the Company sausage eating festival. I am reaching out because" 
  example_title: "festival"
- text: "Good Morning Harold,\n\nI was wondering when the next" 
  example_title: "event"
- text: "URGENT - I need the TPS reports"
  example_title: "URGENT"
- text: "Hi Archibald,\n\nI hope this email finds you extremely well." 
  example_title: "emails that find you"
- text: "Hello there.\n\nI just wanted to reach out and check in to"
  example_title: "checking in"
- 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"
  example_title: "work well"
- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if we could catch up"
  example_title: "catch up"
- 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"
  example_title: "grocery"
parameters:
  min_length: 4
  max_length: 128
  length_penalty: 0.8
  no_repeat_ngram_size: 2
  do_sample: False
  num_beams: 8
  early_stopping: True
  repetition_penalty: 5.5
---


# distilgpt2-emailgen: V2


[![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/pszemraj/d1c2d88b6120cca4ca7df078ea1d1e50/scratchpad.ipynb)

Why write the rest of your email when you can generate it?

```python
from transformers import pipeline

model_tag = "postbot/distilgpt2-emailgen-V2"
generator = pipeline(
              'text-generation', 
              model=model_tag, 
            )
            
prompt = """
Hello, 

Following up on the bubblegum shipment."""

result = generator(
    prompt,
    max_length=64,
    do_sample=False,
    early_stopping=True,
) # generate
print(result[0]['generated_text'])
```

## Model description

This model is a fine-tuned version of `distilgpt2` on the postbot/multi-emails-100k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9126


## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters (run 1/2)

TODO

### Training hyperparameters (run 2/2)

The following hyperparameters were used during training:
- learning_rate: 0.0006
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- 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: 0.01
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9045        | 1.0   | 789  | 2.0006          |
| 1.8115        | 2.0   | 1578 | 1.9557          |
| 1.8501        | 3.0   | 2367 | 1.9110          |
| 1.7376        | 4.0   | 3156 | 1.9126          |


### Framework versions

- Transformers 4.22.2
- Pytorch 1.10.0+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_postbot__distilgpt2-emailgen-V2)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 24.59   |
| ARC (25-shot)         | 20.99          |
| HellaSwag (10-shot)   | 26.78    |
| MMLU (5-shot)         | 25.53         |
| TruthfulQA (0-shot)   | 46.51   |
| Winogrande (5-shot)   | 52.01   |
| GSM8K (5-shot)        | 0.0        |
| DROP (3-shot)         | 0.31         |