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