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RichardErkhov/postbot_-_distilgpt2-emailgen-V2-gguf

sourceHugging Faceupdated 2y agoView on Hugging Face
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Quantization made by Richard Erkhov.

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distilgpt2-emailgen-V2 - GGUF

  • —Model creator: https://huggingface.co/postbot/
  • —Original model: https://huggingface.co/postbot/distilgpt2-emailgen-V2/

Original model description: --- license: apache-2.0 tags:

  • —generatedfromtrainer
  • —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" exampletitle: "grocery" parameters: minlength: 4 maxlength: 128 lengthpenalty: 0.8 norepeatngramsize: 2 dosample: False numbeams: 8 earlystopping: True repetition_penalty: 5.5 ---

distilgpt2-emailgen: V2

![colab](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
  • —trainbatchsize: 16
  • —evalbatchsize: 16
  • —seed: 42
  • —distributed_type: multi-GPU
  • —gradientaccumulationsteps: 8
  • —totaltrainbatch_size: 128
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: cosine
  • —lrschedulerwarmup_ratio: 0.01
  • —num_epochs: 4

Training results

Training LossEpochStepValidation Loss
1.90451.07892.0006
1.81152.015781.9557
1.85013.023671.9110
1.73764.031561.9126

Framework versions

  • —Transformers 4.22.2
  • —Pytorch 1.10.0+cu113
  • —Datasets 2.5.1
  • —Tokenizers 0.12.1

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

MetricValue
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