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--- |
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license: apache-2.0 |
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base_model: pszemraj/random-mega-ar-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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inference: |
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parameters: |
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max_new_tokens: 64 |
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do_sample: true |
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repetition_penalty: 1.1 |
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no_repeat_ngram_size: 5 |
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guidance_scale: 1.01 |
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eta_cutoff: 0.001 |
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widget: |
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- text: "My name is El Microondas the Wise and" |
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example_title: "El Microondas" |
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- text: "A meme is" |
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example_title: "meme" |
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- text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had" |
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example_title: "Coreference resolution" |
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- text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book" |
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example_title: "Logic puzzles" |
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- text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night" |
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example_title: "Reading comprehension" |
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datasets: |
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- pszemraj/simple_wikipedia_LM |
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pipeline_tag: text-generation |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mega-ar-large-2048-simplewiki |
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This is a 'large' size autoregressive MEGA model initialized from random weights and trained on `pszemraj/simple_wikipedia_LM` for three epochs. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.3412 |
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- Accuracy: 0.4360 |
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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: 0.0005 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 80085 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 3.0 |
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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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| 7.2245 | 0.11 | 100 | 6.9372 | 0.0711 | |
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| 6.6575 | 0.22 | 200 | 6.2335 | 0.1853 | |
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| 5.9406 | 0.34 | 300 | 5.3724 | 0.2635 | |
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| 5.4452 | 0.45 | 400 | 4.9243 | 0.2940 | |
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| 5.2524 | 0.56 | 500 | 4.6568 | 0.3172 | |
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| 4.7862 | 0.67 | 600 | 4.4488 | 0.3347 | |
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| 4.7132 | 0.79 | 700 | 4.2699 | 0.3481 | |
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| 4.6601 | 0.9 | 800 | 4.1502 | 0.3582 | |
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| 4.5067 | 1.01 | 900 | 4.0461 | 0.3681 | |
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| 4.4465 | 1.12 | 1000 | 3.9488 | 0.3773 | |
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| 4.4493 | 1.24 | 1100 | 3.8681 | 0.3833 | |
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| 4.3136 | 1.35 | 1200 | 3.8039 | 0.3897 | |
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| 4.2978 | 1.46 | 1300 | 3.7373 | 0.3956 | |
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| 4.0475 | 1.57 | 1400 | 3.6874 | 0.4003 | |
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| 4.1328 | 1.68 | 1500 | 3.6339 | 0.4061 | |
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| 4.0758 | 1.8 | 1600 | 3.5866 | 0.4115 | |
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| 3.8489 | 1.91 | 1700 | 3.5438 | 0.4163 | |
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| 3.913 | 2.02 | 1800 | 3.5136 | 0.4192 | |
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| 3.7746 | 2.13 | 1900 | 3.4860 | 0.4226 | |
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| 3.9547 | 2.25 | 2000 | 3.4505 | 0.4255 | |
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| 3.9726 | 2.36 | 2100 | 3.4283 | 0.4269 | |
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| 3.7546 | 2.47 | 2200 | 3.3999 | 0.4298 | |
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| 3.7442 | 2.58 | 2300 | 3.3820 | 0.4317 | |
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| 3.6848 | 2.7 | 2400 | 3.3687 | 0.4333 | |
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| 3.5491 | 2.81 | 2500 | 3.3531 | 0.4349 | |
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| 3.9563 | 2.92 | 2600 | 3.3412 | 0.4360 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.2.0.dev20230907+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
