|
--- |
|
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 |
|
eta_cutoff: 0.001 |
|
widget: |
|
- text: My name is El Microondas the Wise and |
|
example_title: El Microondas |
|
- text: Kennesaw State University is a public |
|
example_title: Kennesaw State University |
|
- text: Bungie Studios is an American video game developer. They are most famous for developing the award winning Halo series of video games. They also made Destiny. The studio was founded |
|
example_title: Bungie |
|
- text: The Mona Lisa is a world-renowned painting created by |
|
example_title: Mona Lisa |
|
- text: >- |
|
The Harry Potter series, written by J.K. Rowling, begins with the book titled |
|
example_title: Harry Potter Series |
|
- text: >- |
|
Question: I have cities, but no houses. I have mountains, but no trees. |
|
I have water, but no fish. What am I? |
|
|
|
Answer: |
|
example_title: Riddle |
|
- text: The process of photosynthesis involves the conversion of |
|
example_title: Photosynthesis |
|
- text: >- |
|
Jane went to the store to buy some groceries. She picked up apples, oranges, and a loaf of bread. When she got home, she realized she forgot |
|
example_title: Story Continuation |
|
- text: >- |
|
Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, |
|
and another train leaves Station B at 10:00 AM and travels at 80 mph, |
|
when will they meet if the distance between the stations is 300 miles? |
|
|
|
To determine |
|
example_title: Math Problem |
|
- text: >- |
|
In the context of computer programming, an algorithm is |
|
example_title: Algorithm Definition |
|
pipeline_tag: text-generation |
|
datasets: |
|
- pszemraj/simple_wikipedia_LM |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# 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 |
