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---
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
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