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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- postbot/multi-emails-hq |
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metrics: |
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- accuracy |
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model-index: |
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- name: pythia-160m-hq-emails-v4 |
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results: |
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- task: |
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name: Causal Language Modeling |
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type: text-generation |
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dataset: |
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name: postbot/multi-emails-hq |
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type: postbot/multi-emails-hq |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.611281497151223 |
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widget: |
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- text: >- |
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Good Morning Professor Beans, |
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Hope you are doing well. I just wanted to reach out and ask if differential |
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calculus will be on the exam |
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example_title: email to prof |
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- text: >- |
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Hey <NAME>, |
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Thank you for signing up for my weekly newsletter. Before we get started, |
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you'll have to confirm your email address. |
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example_title: newsletter |
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- text: >- |
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Hi <NAME>, |
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I hope this email finds you well. I wanted to reach out and ask about office |
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hours |
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example_title: office hours |
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- text: >- |
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Greetings <NAME>, |
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I hope you had a splendid evening at the Company sausage eating festival. I |
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am reaching out because |
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example_title: festival |
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- text: |- |
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Good Morning Harold, |
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I was wondering when the next |
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example_title: event |
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- text: URGENT - I need the TPS reports |
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example_title: URGENT |
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- text: |- |
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Hi Archibald, |
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I hope this email finds you extremely well. |
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example_title: emails that find you |
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- text: |- |
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Hello there. |
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I just wanted to reach out and check in to |
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example_title: checking in |
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- text: >- |
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Hello <NAME>, |
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I hope this email finds you well. I wanted to reach out and see if you've |
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enjoyed your time with us |
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example_title: work well |
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- text: >- |
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Hi <NAME>, |
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I hope this email finds you well. I wanted to reach out and see if we could |
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catch up |
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example_title: catch up |
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- text: >- |
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I'm <NAME> and I just moved into the area and wanted to reach out and get |
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some details on where I could get groceries and |
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example_title: grocery |
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inference: |
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parameters: |
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min_length: 16 |
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max_length: 64 |
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no_repeat_ngram_size: 4 |
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do_sample: true |
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top_k: 40 |
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top_p: 0.95 |
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repetition_penalty: 3.5 |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# pythia-160m-hq-emails-v4 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.2856 |
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- Accuracy: 0.6113 |
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## Model description |
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this is v4 |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0006 |
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- train_batch_size: 4 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 4.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 2.412 | 0.99 | 76 | 2.5027 | 0.5458 | |
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| 1.9702 | 1.99 | 152 | 2.2757 | 0.5850 | |
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| 1.4628 | 2.99 | 228 | 2.2162 | 0.6082 | |
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| 1.1662 | 3.99 | 304 | 2.2856 | 0.6113 | |
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### Framework versions |
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- Transformers 4.27.0.dev0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.1 |
