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---
license: apache-2.0
tags:
- generated_from_trainer
- text generation
- 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>,
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. I wanted to reach out and ask about office hours'
example_title: office hours
- 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 Harold,
I was wondering when the next'
example_title: event
- text: URGENT - I need the TPS reports
example_title: URGENT
- text: 'Hi Archibald,
I hope this email finds you extremely well.'
example_title: emails that find you
- text: 'Hello there.
I just wanted to reach out and check in to'
example_title: checking in
- text: 'Hello <NAME>,
I 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>,
I 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: 32
max_length: 128
no_repeat_ngram_size: 2
do_sample: true
temperature: 0.4
top_k: 30
top_p: 0.9
repetition_penalty: 3.5
length_penalty: 0.9
base_model: EleutherAI/gpt-neo-1.3B
model-index:
- name: gpt-neo-1.3B-emailgen
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 29.95
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=postbot/gpt-neo-1.3B-emailgen
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 47.95
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=postbot/gpt-neo-1.3B-emailgen
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 24.11
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=postbot/gpt-neo-1.3B-emailgen
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 42.55
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=postbot/gpt-neo-1.3B-emailgen
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 56.27
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=postbot/gpt-neo-1.3B-emailgen
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0.0
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=postbot/gpt-neo-1.3B-emailgen
name: Open LLM Leaderboard
---
# gpt-neo-1.3B-emailgen
This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) on the postbot/multi-emails-100k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6930
## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- 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: 0.02
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8669 | 1.0 | 789 | 1.7866 |
| 1.4049 | 2.0 | 1578 | 1.6930 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0+cu113
- 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__gpt-neo-1.3B-emailgen)
| Metric |Value|
|---------------------------------|----:|
|Avg. |33.47|
|AI2 Reasoning Challenge (25-Shot)|29.95|
|HellaSwag (10-Shot) |47.95|
|MMLU (5-Shot) |24.11|
|TruthfulQA (0-shot) |42.55|
|Winogrande (5-shot) |56.27|
|GSM8k (5-shot) | 0.00|
|