---
license: other
tags:
- generated_from_trainer
- opt
- custom-license
- no-commercial
- email
- auto-complete
- 125m
datasets:
- aeslc

widget:
- 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. Let me start by saying that I am a big fan of your work." 
  example_title: "fan"
- 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 <NAME>,\n\nI 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](https://huggingface.co./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