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pszemraj
/
opt-125m-email-generation

Text Generation
Transformers
PyTorch
Safetensors
opt
Generated from Trainer
custom-license
non-commercial
email
auto-complete
125m
text-generation-inference
Inference Endpoints
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opt-125m-email-generation / README.md
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metadata
license: other
tags:
  - generated_from_trainer
  - opt
  - custom-license
  - no-commercial
  - email
  - auto-complete
  - 125m
datasets:
  - aeslc
widget:
  - text: >-
      Hey <NAME>,


      Thank 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>,


      I hope this email finds you well. Let me start by saying that I am a big
      fan of your work.
    example_title: fan
  - text: >-
      Greetings <NAME>,


      I hope you had a splendid evening at the Company sausage eating festival.
      I am reaching out because
    example_title: festival
  - text: |-
      Good Morning <NAME>,

      I was just thinking to myself about how much I love creating value
    example_title: value
  - text: URGENT - I need
    example_title: URGENT
parameters:
  min_length: 4
  max_length: 64
  length_penalty: 0.7
  no_repeat_ngram_size: 3
  do_sample: false
  num_beams: 4
  early_stopping: true
  repetition_penalty: 3.5
  use_fast: false

opt-125m-emailgen-v2_DS-aeslc_Ep-4_Bs-8

This model is a fine-tuned version of facebook/opt-125m on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.5552

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0004
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss
2.8245 1.0 129 2.8030
2.521 2.0 258 2.6343
2.2074 3.0 387 2.5595
2.0145 4.0 516 2.5552

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.11.0+cu113
  • Tokenizers 0.12.1