allenai/bhaskara

Model

should probably proofread and complete it, then remove this comment. -->

5 commits

2 linked in READMEs

updated Mar 6, 2023

See the code

README

output

Model description

This model is a fine-tuned version of EleutherAI/gpt-neo-2.7B on the Lila-IID-train/dev set from the Lila dataset.

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

endpoints_compatible

generated_from_trainer

gpt_neo

pytorch

text-generation

transformers

Contributors

mattf1n

3 commits

joaogante

1 commits

Matthew Finlayson

1 commits

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Linked in READMEs

yuzhimanhua/Awesome-Scientific-Language-Models

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allenai/lila

A unified benchmark for math reasoning

README badge

allenai/bhaskara

Model

should probably proofread and complete it, then remove this comment. -->

5 commits

2 linked in READMEs

updated Mar 6, 2023

See the code

README

output

Model description

This model is a fine-tuned version of EleutherAI/gpt-neo-2.7B on the Lila-IID-train/dev set from the Lila dataset.

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