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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output
results: []
---
<!-- 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. -->
# output
## Model description
This model is a fine-tuned version of [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) on the Lila-IID-train/dev set from the [Lila dataset](https://github.com/allenai/Lila).
## Usage
Bhaskara was trained with the following format:
~~~
Question: ...
Answer: ...
Program:
```python
...
```
~~~
It will perform best if queried in this way.
## Intended uses & limitations
If you use this model, please cite our work.
```
@INPROCEEDINGS{Mishra2022Lila,
author = {
Swaroop Mishra
and Matthew Finlayson
and Pan Lu
and Leonard Tang
and Sean Welleck
and Chitta Baral
and Tanmay Rajpurohit
and Oyvind Tafjord
and Ashish Sabharwal
and Peter Clark
and Ashwin Kalyan},
title = {Lila: A Unified Benchmark for Mathematical Reasoning},
booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
year = {2022}
}
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log | 0.06 | 100 | 0.7930 | 0.8214 |
| No log | 0.11 | 200 | 0.7544 | 0.8290 |
| No log | 0.17 | 300 | 0.7358 | 0.8328 |
| No log | 0.23 | 400 | 0.7192 | 0.8357 |
| 0.8156 | 0.28 | 500 | 0.7012 | 0.8397 |
| 0.8156 | 0.34 | 600 | 0.6904 | 0.8419 |
| 0.8156 | 0.4 | 700 | 0.6802 | 0.8440 |
| 0.8156 | 0.45 | 800 | 0.6670 | 0.8465 |
| 0.8156 | 0.51 | 900 | 0.6572 | 0.8486 |
| 0.7219 | 0.57 | 1000 | 0.6499 | 0.8500 |
| 0.7219 | 0.62 | 1100 | 0.6411 | 0.8522 |
| 0.7219 | 0.68 | 1200 | 0.6343 | 0.8537 |
| 0.7219 | 0.74 | 1300 | 0.6299 | 0.8546 |
| 0.7219 | 0.79 | 1400 | 0.6221 | 0.8561 |
| 0.662 | 0.85 | 1500 | 0.6157 | 0.8574 |
| 0.662 | 0.91 | 1600 | 0.6138 | 0.8579 |
| 0.662 | 0.96 | 1700 | 0.6055 | 0.8595 |
| 0.662 | 1.02 | 1800 | 0.6143 | 0.8598 |
| 0.662 | 1.08 | 1900 | 0.6191 | 0.8599 |
| 0.5707 | 1.14 | 2000 | 0.6118 | 0.8607 |
| 0.5707 | 1.19 | 2100 | 0.6123 | 0.8611 |
| 0.5707 | 1.25 | 2200 | 0.6089 | 0.8617 |
| 0.5707 | 1.31 | 2300 | 0.6064 | 0.8619 |
| 0.5707 | 1.36 | 2400 | 0.6079 | 0.8625 |
| 0.4923 | 1.42 | 2500 | 0.6040 | 0.8625 |
| 0.4923 | 1.48 | 2600 | 0.6030 | 0.8630 |
| 0.4923 | 1.53 | 2700 | 0.6021 | 0.8636 |
| 0.4923 | 1.59 | 2800 | 0.6001 | 0.8643 |
| 0.4923 | 1.65 | 2900 | 0.5981 | 0.8644 |
| 0.4909 | 1.7 | 3000 | 0.5942 | 0.8648 |
| 0.4909 | 1.76 | 3100 | 0.5918 | 0.8650 |
| 0.4909 | 1.82 | 3200 | 0.5923 | 0.8659 |
| 0.4909 | 1.87 | 3300 | 0.5884 | 0.8664 |
| 0.4909 | 1.93 | 3400 | 0.5884 | 0.8663 |
| 0.4964 | 1.99 | 3500 | 0.5903 | 0.8669 |
| 0.4964 | 2.04 | 3600 | 0.6421 | 0.8655 |
| 0.4964 | 2.1 | 3700 | 0.6401 | 0.8651 |
| 0.4964 | 2.16 | 3800 | 0.6411 | 0.8649 |
| 0.4964 | 2.21 | 3900 | 0.6387 | 0.8645 |
| 0.345 | 2.27 | 4000 | 0.6362 | 0.8654 |
| 0.345 | 2.33 | 4100 | 0.6362 | 0.8654 |
| 0.345 | 2.38 | 4200 | 0.6362 | 0.8654 |
| 0.345 | 2.44 | 4300 | 0.6357 | 0.8655 |
| 0.345 | 2.5 | 4400 | 0.6362 | 0.8656 |
| 0.3463 | 2.55 | 4500 | 0.6377 | 0.8658 |
| 0.3463 | 2.61 | 4600 | 0.6357 | 0.8660 |
| 0.3463 | 2.67 | 4700 | 0.6294 | 0.8665 |
| 0.3463 | 2.72 | 4800 | 0.6333 | 0.8665 |
| 0.3463 | 2.78 | 4900 | 0.6362 | 0.8662 |
| 0.3508 | 2.84 | 5000 | 0.6357 | 0.8666 |
| 0.3508 | 2.89 | 5100 | 0.6299 | 0.8673 |
| 0.3508 | 2.95 | 5200 | 0.6313 | 0.8668 |
| 0.3508 | 3.01 | 5300 | 0.7188 | 0.8646 |
| 0.3508 | 3.06 | 5400 | 0.7017 | 0.8656 |
| 0.295 | 3.12 | 5500 | 0.6982 | 0.8653 |
| 0.295 | 3.18 | 5600 | 0.7031 | 0.8655 |
| 0.295 | 3.23 | 5700 | 0.6992 | 0.8651 |
| 0.295 | 3.29 | 5800 | 0.6997 | 0.8653 |
| 0.295 | 3.35 | 5900 | 0.7041 | 0.8651 |
| 0.2348 | 3.41 | 6000 | 0.7075 | 0.8649 |
| 0.2348 | 3.46 | 6100 | 0.6992 | 0.8650 |
| 0.2348 | 3.52 | 6200 | 0.7065 | 0.8647 |
| 0.2348 | 3.58 | 6300 | 0.6997 | 0.8652 |
| 0.2348 | 3.63 | 6400 | 0.7026 | 0.8651 |
| 0.2411 | 3.69 | 6500 | 0.7046 | 0.8656 |
| 0.2411 | 3.75 | 6600 | 0.7007 | 0.8655 |
| 0.2411 | 3.8 | 6700 | 0.7026 | 0.8651 |
| 0.2411 | 3.86 | 6800 | 0.7031 | 0.8655 |
| 0.2411 | 3.92 | 6900 | 0.7012 | 0.8658 |
| 0.251 | 3.97 | 7000 | 0.7051 | 0.8656 |
| 0.251 | 4.03 | 7100 | 0.7607 | 0.8650 |
| 0.251 | 4.09 | 7200 | 0.7632 | 0.8656 |
| 0.251 | 4.14 | 7300 | 0.7588 | 0.8655 |
| 0.251 | 4.2 | 7400 | 0.7578 | 0.8651 |
| 0.1797 | 4.26 | 7500 | 0.7710 | 0.8645 |
| 0.1797 | 4.31 | 7600 | 0.7627 | 0.8648 |
| 0.1797 | 4.37 | 7700 | 0.7583 | 0.8650 |
| 0.1797 | 4.43 | 7800 | 0.7646 | 0.8649 |
| 0.1797 | 4.48 | 7900 | 0.7598 | 0.8646 |
| 0.1784 | 4.54 | 8000 | 0.7656 | 0.8650 |
| 0.1784 | 4.6 | 8100 | 0.7617 | 0.8648 |
| 0.1784 | 4.65 | 8200 | 0.7573 | 0.8651 |
| 0.1784 | 4.71 | 8300 | 0.7671 | 0.8648 |
| 0.1784 | 4.77 | 8400 | 0.7563 | 0.8651 |
| 0.1827 | 4.82 | 8500 | 0.7651 | 0.8649 |
| 0.1827 | 4.88 | 8600 | 0.7637 | 0.8650 |
| 0.1827 | 4.94 | 8700 | 0.7607 | 0.8654 |
| 0.1827 | 4.99 | 8800 | 0.7607 | 0.8650 |
| 0.1827 | 5.05 | 8900 | 0.8149 | 0.8646 |
| 0.167 | 5.11 | 9000 | 0.8081 | 0.8648 |
| 0.167 | 5.16 | 9100 | 0.8184 | 0.8644 |
| 0.167 | 5.22 | 9200 | 0.8140 | 0.8647 |
| 0.167 | 5.28 | 9300 | 0.8169 | 0.8644 |
| 0.167 | 5.33 | 9400 | 0.8120 | 0.8645 |
| 0.1371 | 5.39 | 9500 | 0.8154 | 0.8643 |
| 0.1371 | 5.45 | 9600 | 0.8179 | 0.8642 |
| 0.1371 | 5.51 | 9700 | 0.8154 | 0.8643 |
| 0.1371 | 5.56 | 9800 | 0.8120 | 0.8645 |
| 0.1371 | 5.62 | 9900 | 0.8110 | 0.8650 |
| 0.1425 | 5.68 | 10000 | 0.8159 | 0.8645 |
| 0.1425 | 5.73 | 10100 | 0.8174 | 0.8646 |
| 0.1425 | 5.79 | 10200 | 0.8159 | 0.8649 |
| 0.1425 | 5.85 | 10300 | 0.8110 | 0.8639 |
| 0.1425 | 5.9 | 10400 | 0.8135 | 0.8645 |
| 0.1505 | 5.96 | 10500 | 0.8140 | 0.8642 |
| 0.1505 | 6.02 | 10600 | 0.8628 | 0.8640 |
| 0.1505 | 6.07 | 10700 | 0.8540 | 0.8644 |
| 0.1505 | 6.13 | 10800 | 0.8530 | 0.8642 |
| 0.1505 | 6.19 | 10900 | 0.8560 | 0.8647 |
| 0.1086 | 6.24 | 11000 | 0.8555 | 0.8649 |
| 0.1086 | 6.3 | 11100 | 0.8604 | 0.8644 |
| 0.1086 | 6.36 | 11200 | 0.8569 | 0.8642 |
| 0.1086 | 6.41 | 11300 | 0.8530 | 0.8639 |
| 0.1086 | 6.47 | 11400 | 0.8589 | 0.8643 |
| 0.1076 | 6.53 | 11500 | 0.8525 | 0.8639 |
| 0.1076 | 6.58 | 11600 | 0.8579 | 0.8640 |
| 0.1076 | 6.64 | 11700 | 0.8594 | 0.8640 |
| 0.1076 | 6.7 | 11800 | 0.8599 | 0.8643 |
| 0.1076 | 6.75 | 11900 | 0.8564 | 0.8640 |
| 0.1109 | 6.81 | 12000 | 0.8633 | 0.8640 |
| 0.1109 | 6.87 | 12100 | 0.8584 | 0.8638 |
| 0.1109 | 6.92 | 12200 | 0.8647 | 0.8636 |
| 0.1109 | 6.98 | 12300 | 0.8599 | 0.8635 |
| 0.1109 | 7.04 | 12400 | 0.8979 | 0.8632 |
| 0.1028 | 7.09 | 12500 | 0.8936 | 0.8635 |
| 0.1028 | 7.15 | 12600 | 0.9043 | 0.8637 |
| 0.1028 | 7.21 | 12700 | 0.8989 | 0.8642 |
| 0.1028 | 7.26 | 12800 | 0.8936 | 0.8642 |
| 0.1028 | 7.32 | 12900 | 0.8921 | 0.8641 |
| 0.0774 | 7.38 | 13000 | 0.8955 | 0.8634 |
| 0.0774 | 7.43 | 13100 | 0.8950 | 0.8636 |
| 0.0774 | 7.49 | 13200 | 0.8994 | 0.8635 |
| 0.0774 | 7.55 | 13300 | 0.8999 | 0.8635 |
| 0.0774 | 7.6 | 13400 | 0.8936 | 0.8631 |
| 0.0852 | 7.66 | 13500 | 0.9048 | 0.8634 |
| 0.0852 | 7.72 | 13600 | 0.8960 | 0.8632 |
| 0.0852 | 7.78 | 13700 | 0.9023 | 0.8635 |
| 0.0852 | 7.83 | 13800 | 0.8984 | 0.8638 |
| 0.0852 | 7.89 | 13900 | 0.9019 | 0.8635 |
| 0.0879 | 7.95 | 14000 | 0.9014 | 0.8634 |
| 0.0879 | 8.0 | 14100 | 0.9136 | 0.8630 |
| 0.0879 | 8.06 | 14200 | 0.9312 | 0.8639 |
| 0.0879 | 8.12 | 14300 | 0.9346 | 0.8635 |
| 0.0879 | 8.17 | 14400 | 0.9307 | 0.8635 |
| 0.0611 | 8.23 | 14500 | 0.9419 | 0.8641 |
| 0.0611 | 8.29 | 14600 | 0.9331 | 0.8631 |
| 0.0611 | 8.34 | 14700 | 0.9375 | 0.8636 |
| 0.0611 | 8.4 | 14800 | 0.9292 | 0.8626 |
| 0.0611 | 8.46 | 14900 | 0.9458 | 0.8637 |
| 0.061 | 8.51 | 15000 | 0.9336 | 0.8634 |
| 0.061 | 8.57 | 15100 | 0.9409 | 0.8630 |
| 0.061 | 8.63 | 15200 | 0.9390 | 0.8632 |
| 0.061 | 8.68 | 15300 | 0.9375 | 0.8628 |
| 0.061 | 8.74 | 15400 | 0.9365 | 0.8630 |
| 0.0646 | 8.8 | 15500 | 0.9370 | 0.8628 |
| 0.0646 | 8.85 | 15600 | 0.9355 | 0.8629 |
| 0.0646 | 8.91 | 15700 | 0.9375 | 0.8632 |
| 0.0646 | 8.97 | 15800 | 0.9390 | 0.8630 |
| 0.0646 | 9.02 | 15900 | 0.9717 | 0.8630 |
| 0.0593 | 9.08 | 16000 | 0.9673 | 0.8626 |
| 0.0593 | 9.14 | 16100 | 0.9644 | 0.8630 |
| 0.0593 | 9.19 | 16200 | 0.9624 | 0.8631 |
| 0.0593 | 9.25 | 16300 | 0.9648 | 0.8633 |
| 0.0593 | 9.31 | 16400 | 0.9673 | 0.8632 |
| 0.0415 | 9.36 | 16500 | 0.9658 | 0.8633 |
| 0.0415 | 9.42 | 16600 | 0.9688 | 0.8628 |
| 0.0415 | 9.48 | 16700 | 0.9653 | 0.8632 |
| 0.0415 | 9.53 | 16800 | 0.9658 | 0.8628 |
| 0.0415 | 9.59 | 16900 | 0.9668 | 0.8629 |
| 0.0471 | 9.65 | 17000 | 0.9604 | 0.8625 |
| 0.0471 | 9.7 | 17100 | 0.9658 | 0.8621 |
| 0.0471 | 9.76 | 17200 | 0.9731 | 0.8630 |
| 0.0471 | 9.82 | 17300 | 0.9692 | 0.8626 |
| 0.0471 | 9.88 | 17400 | 0.9673 | 0.8623 |
| 0.0528 | 9.93 | 17500 | 0.9614 | 0.8620 |
| 0.0528 | 9.99 | 17600 | 0.9697 | 0.8621 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|