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--- |
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license: other |
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tags: |
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- generated_from_trainer |
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- opt |
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- custom-license |
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- no-commercial |
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- email |
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- auto-complete |
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- 125m |
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datasets: |
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- aeslc |
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widget: |
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- text: "Hey <NAME>,\n\nThank you for signing up for my weekly newsletter. Before we get started, you'll have to confirm your email address." |
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example_title: "newsletter" |
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- text: "Hi <NAME>,\n\nI hope this email finds you well. Let me start by saying that I am a big fan of your work." |
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example_title: "fan" |
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- text: "Greetings <NAME>,\n\nI hope you had a splendid evening at the Company sausage eating festival. I am reaching out because" |
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example_title: "festival" |
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- text: "Good Morning <NAME>,\n\nI was just thinking to myself about how much I love creating value" |
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example_title: "value" |
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- text: "URGENT - I need" |
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example_title: "URGENT" |
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parameters: |
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min_length: 4 |
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max_length: 64 |
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length_penalty: 0.7 |
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no_repeat_ngram_size: 3 |
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do_sample: False |
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num_beams: 4 |
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early_stopping: True |
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repetition_penalty: 3.5 |
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use_fast: False |
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--- |
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# opt-125m-emailgen-v2_DS-aeslc_Ep-4_Bs-8 |
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This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5552 |
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## Model description |
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More information needed |
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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: 0.0004 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 16 |
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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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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.8245 | 1.0 | 129 | 2.8030 | |
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| 2.521 | 2.0 | 258 | 2.6343 | |
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| 2.2074 | 3.0 | 387 | 2.5595 | |
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| 2.0145 | 4.0 | 516 | 2.5552 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu113 |
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- Tokenizers 0.12.1 |
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