postbot_-_pythia-160m-hq-emails-gguf

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pythia-160m-hq-emails - GGUF

• Model creator: https://huggingface.co/postbot/
• Original model: https://huggingface.co/postbot/pythia-160m-hq-emails/

Original model description:

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

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pythia-160m-hq-emails-v4

This model is a fine-tuned version of 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

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

Detailed results can be found here