---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- postbot/multi-emails-hq
metrics:
- accuracy
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
inference:
  parameters:
    min_length: 16
    max_length: 64
    no_repeat_ngram_size: 4
    do_sample: true
    top_k: 40
    top_p: 0.95
    repetition_penalty: 3.5
pipeline_tag: text-generation
base_model: EleutherAI/pythia-160m-deduped
model-index:
- name: pythia-160m-hq-emails-v4
  results:
  - task:
      type: text-generation
      name: Causal Language Modeling
    dataset:
      name: postbot/multi-emails-hq
      type: postbot/multi-emails-hq
    metrics:
    - type: accuracy
      value: 0.611281497151223
      name: Accuracy
---


# pythia-160m-hq-emails-v4

This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) on the postbot/multi-emails-hq dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2856
- Accuracy: 0.6113
- perplexity: 9.8313

## Model description

this is v4

## 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: 0.0006
- train_batch_size: 4
- eval_batch_size: 1
- 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: 0.05
- num_epochs: 4.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.412         | 0.99  | 76   | 2.5027          | 0.5458   |
| 1.9702        | 1.99  | 152  | 2.2757          | 0.5850   |
| 1.4628        | 2.99  | 228  | 2.2162          | 0.6082   |
| 1.1662        | 3.99  | 304  | 2.2856          | 0.6113   |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.1
# [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__pythia-160m-hq-emails)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 25.12   |
| ARC (25-shot)         | 23.12          |
| HellaSwag (10-shot)   | 30.05    |
| MMLU (5-shot)         | 26.58         |
| TruthfulQA (0-shot)   | 45.51   |
| Winogrande (5-shot)   | 50.28   |
| GSM8K (5-shot)        | 0.0        |
| DROP (3-shot)         | 0.31         |