readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - postbot/multi-emails-hq
  metrics:
  - accuracy
  model-index:
  - name: pythia-160m-hq-emails-v4
    results:
    - task:
        name: Causal Language Modeling
        type: text-generation
      dataset:
        name: postbot/multi-emails-hq
        type: postbot/multi-emails-hq
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.611281497151223
  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
  license: apache-2.0
  language:
  - en
  pipeline_tag: text-generation
  ---
  
  
  # 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