Downloads · 30 days
21
23% of all-time downloads
latin_gpt2
gaodrew/latin_gpt2
latin_gpt2 is a text generation model from gaodrew. Use it when you need the model to write or continue text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
All-time downloads
90
Public
Parameters
93.4M
12.1 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors374 MB · 100%
At a glance
- Task
- Text Generation
- Library
- transformers
- Model type
- gpt2
- Access
- Public
- Created
- Jun 16, 2024
- Updated
- Jun 17, 2024
- SHA
- 2838fab3
Try a prompt
Model card
From the Hugging Face model README
<!-- 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. -->
latin_gpt2
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 5.4614
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: 512
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 3000
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 7.172 | 0.0057 | 500 | 7.1975 |
| 6.7223 | 0.0115 | 1000 | 6.9021 |
| 6.4626 | 0.0172 | 1500 | 6.6753 |
| 6.2743 | 0.0230 | 2000 | 6.5041 |
| 6.1669 | 0.0287 | 2500 | 6.3793 |
| 6.1212 | 0.0344 | 3000 | 6.3379 |
| 6.0511 | 0.0402 | 3500 | 6.3103 |
| 5.9919 | 0.0459 | 4000 | 6.2384 |
| 5.9364 | 0.0516 | 4500 | 6.2122 |
| 5.887 | 0.0574 | 5000 | 6.1205 |
| 5.8564 | 0.0631 | 5500 | 6.1243 |
| 5.8264 | 0.0689 | 6000 | 6.0895 |
| 5.7942 | 0.0746 | 6500 | 6.0713 |
| 5.7826 | 0.0803 | 7000 | 6.0359 |
| 5.7475 | 0.0861 | 7500 | 6.0351 |
| 5.7329 | 0.0918 | 8000 | 6.0039 |
| 5.7127 | 0.0975 | 8500 | 5.9915 |
| 5.6971 | 0.1033 | 9000 | 5.9835 |
| 5.6825 | 0.1090 | 9500 | 5.9810 |
| 5.6695 | 0.1148 | 10000 | 5.9542 |
| 5.6537 | 0.1205 | 10500 | 5.9367 |
| 5.6455 | 0.1262 | 11000 | 5.9178 |
| 5.6254 | 0.1320 | 11500 | 5.9097 |
| 5.62 | 0.1377 | 12000 | 5.9092 |
| 5.608 | 0.1434 | 12500 | 5.8881 |
| 5.5993 | 0.1492 | 13000 | 5.8807 |
| 5.5891 | 0.1549 | 13500 | 5.8707 |
| 5.5791 | 0.1607 | 14000 | 5.8809 |
| 5.5701 | 0.1664 | 14500 | 5.8585 |
| 5.5651 | 0.1721 | 15000 | 5.8436 |
| 5.5595 | 0.1779 | 15500 | 5.8607 |
| 5.5509 | 0.1836 | 16000 | 5.8308 |
| 5.5401 | 0.1894 | 16500 | 5.8381 |
| 5.535 | 0.1951 | 17000 | 5.8749 |
| 5.5281 | 0.2008 | 17500 | 5.8331 |
| 5.5231 | 0.2066 | 18000 | 5.8139 |
| 5.5148 | 0.2123 | 18500 | 5.8078 |
| 5.5112 | 0.2180 | 19000 | 5.8016 |
| 5.5049 | 0.2238 | 19500 | 5.8034 |
| 5.5006 | 0.2295 | 20000 | 5.8025 |
| 5.4909 | 0.2353 | 20500 | 5.8017 |
| 5.4835 | 0.2410 | 21000 | 5.7782 |
| 5.4841 | 0.2467 | 21500 | 5.7862 |
| 5.4794 | 0.2525 | 22000 | 5.7690 |
| 5.476 | 0.2582 | 22500 | 5.7689 |
| 5.4668 | 0.2639 | 23000 | 5.7806 |
| 5.4585 | 0.2697 | 23500 | 5.7678 |
| 5.4573 | 0.2754 | 24000 | 5.7499 |
| 5.4551 | 0.2812 | 24500 | 5.7696 |
| 5.451 | 0.2869 | 25000 | 5.7564 |
| 5.4465 | 0.2926 | 25500 | 5.7508 |
| 5.4396 | 0.2984 | 26000 | 5.7414 |
| 5.4356 | 0.3041 | 26500 | 5.7354 |
| 5.4321 | 0.3098 | 27000 | 5.7471 |
| 5.427 | 0.3156 | 27500 | 5.7296 |
| 5.4242 | 0.3213 | 28000 | 5.7294 |
| 5.4192 | 0.3271 | 28500 | 5.7252 |
| 5.4168 | 0.3328 | 29000 | 5.7183 |
| 5.4135 | 0.3385 | 29500 | 5.7241 |
| 5.4077 | 0.3443 | 30000 | 5.7148 |
| 5.4051 | 0.3500 | 30500 | 5.7215 |
| 5.3994 | 0.3557 | 31000 | 5.7140 |
| 5.3992 | 0.3615 | 31500 | 5.7079 |
| 5.3902 | 0.3672 | 32000 | 5.7057 |
| 5.3848 | 0.3730 | 32500 | 5.7047 |
| 5.3865 | 0.3787 | 33000 | 5.6973 |
| 5.3824 | 0.3844 | 33500 | 5.6938 |
| 5.3769 | 0.3902 | 34000 | 5.6950 |
| 5.3733 | 0.3959 | 34500 | 5.6885 |
| 5.3694 | 0.4017 | 35000 | 5.6819 |
| 5.3638 | 0.4074 | 35500 | 5.6770 |
| 5.3611 | 0.4131 | 36000 | 5.6819 |
| 5.3615 | 0.4189 | 36500 | 5.6705 |
| 5.354 | 0.4246 | 37000 | 5.6757 |
| 5.3522 | 0.4303 | 37500 | 5.6718 |
| 5.3422 | 0.4361 | 38000 | 5.6679 |
| 5.343 | 0.4418 | 38500 | 5.6655 |
| 5.3434 | 0.4476 | 39000 | 5.6591 |
| 5.3385 | 0.4533 | 39500 | 5.6608 |
| 5.3325 | 0.4590 | 40000 | 5.6629 |
| 5.3315 | 0.4648 | 40500 | 5.6581 |
| 5.3317 | 0.4705 | 41000 | 5.6534 |
| 5.3275 | 0.4762 | 41500 | 5.6447 |
| 5.3202 | 0.4820 | 42000 | 5.6451 |
| 5.3149 | 0.4877 | 42500 | 5.6348 |
| 5.313 | 0.4935 | 43000 | 5.6366 |
| 5.3122 | 0.4992 | 43500 | 5.6384 |
| 5.3065 | 0.5049 | 44000 | 5.6326 |
| 5.299 | 0.5107 | 44500 | 5.6226 |
| 5.2997 | 0.5164 | 45000 | 5.6301 |
| 5.2959 | 0.5221 | 45500 | 5.6172 |
| 5.2907 | 0.5279 | 46000 | 5.6232 |
| 5.2889 | 0.5336 | 46500 | 5.6239 |
| 5.288 | 0.5394 | 47000 | 5.6069 |
| 5.277 | 0.5451 | 47500 | 5.6154 |
| 5.2765 | 0.5508 | 48000 | 5.6157 |
| 5.2739 | 0.5566 | 48500 | 5.6035 |
| 5.2693 | 0.5623 | 49000 | 5.6009 |
| 5.2635 | 0.5681 | 49500 | 5.5978 |
| 5.2682 | 0.5738 | 50000 | 5.5987 |
| 5.258 | 0.5795 | 50500 | 5.5971 |
| 5.26 | 0.5853 | 51000 | 5.5994 |
| 5.2568 | 0.5910 | 51500 | 5.5873 |
| 5.2446 | 0.5967 | 52000 | 5.5771 |
| 5.2469 | 0.6025 | 52500 | 5.5824 |
| 5.2459 | 0.6082 | 53000 | 5.5853 |
| 5.239 | 0.6140 | 53500 | 5.5781 |
| 5.2355 | 0.6197 | 54000 | 5.5729 |
| 5.2296 | 0.6254 | 54500 | 5.5737 |
| 5.2301 | 0.6312 | 55000 | 5.5656 |
| 5.2277 | 0.6369 | 55500 | 5.5716 |
| 5.2197 | 0.6426 | 56000 | 5.5583 |
| 5.2131 | 0.6484 | 56500 | 5.5639 |
| 5.2132 | 0.6541 | 57000 | 5.5537 |
| 5.2103 | 0.6599 | 57500 | 5.5656 |
| 5.2078 | 0.6656 | 58000 | 5.5524 |
| 5.203 | 0.6713 | 58500 | 5.5470 |
| 5.2035 | 0.6771 | 59000 | 5.5454 |
| 5.1963 | 0.6828 | 59500 | 5.5428 |
| 5.1932 | 0.6885 | 60000 | 5.5355 |
| 5.1906 | 0.6943 | 60500 | 5.5338 |
| 5.1864 | 0.7000 | 61000 | 5.5352 |
| 5.1823 | 0.7058 | 61500 | 5.5295 |
| 5.179 | 0.7115 | 62000 | 5.5296 |
| 5.1752 | 0.7172 | 62500 | 5.5259 |
| 5.1726 | 0.7230 | 63000 | 5.5291 |
| 5.1692 | 0.7287 | 63500 | 5.5183 |
| 5.1694 | 0.7345 | 64000 | 5.5173 |
| 5.1624 | 0.7402 | 64500 | 5.5162 |
| 5.161 | 0.7459 | 65000 | 5.5104 |
| 5.1588 | 0.7517 | 65500 | 5.5145 |
| 5.156 | 0.7574 | 66000 | 5.5057 |
| 5.1539 | 0.7631 | 66500 | 5.5036 |
| 5.1474 | 0.7689 | 67000 | 5.5093 |
| 5.1457 | 0.7746 | 67500 | 5.5052 |
| 5.1444 | 0.7804 | 68000 | 5.4979 |
| 5.1437 | 0.7861 | 68500 | 5.4979 |
| 5.1402 | 0.7918 | 69000 | 5.5009 |
| 5.1346 | 0.7976 | 69500 | 5.4907 |
| 5.1308 | 0.8033 | 70000 | 5.4905 |
| 5.1325 | 0.8090 | 70500 | 5.4880 |
| 5.1297 | 0.8148 | 71000 | 5.4866 |
| 5.1246 | 0.8205 | 71500 | 5.4871 |
| 5.1232 | 0.8263 | 72000 | 5.4846 |
| 5.1232 | 0.8320 | 72500 | 5.4840 |
| 5.1219 | 0.8377 | 73000 | 5.4811 |
| 5.1126 | 0.8435 | 73500 | 5.4791 |
| 5.1168 | 0.8492 | 74000 | 5.4782 |
| 5.1175 | 0.8549 | 74500 | 5.4763 |
| 5.1108 | 0.8607 | 75000 | 5.4771 |
| 5.1082 | 0.8664 | 75500 | 5.4742 |
| 5.1053 | 0.8722 | 76000 | 5.4738 |
| 5.107 | 0.8779 | 76500 | 5.4718 |
| 5.1074 | 0.8836 | 77000 | 5.4694 |
| 5.1074 | 0.8894 | 77500 | 5.4692 |
| 5.1038 | 0.8951 | 78000 | 5.4697 |
| 5.105 | 0.9008 | 78500 | 5.4684 |
| 5.1019 | 0.9066 | 79000 | 5.4665 |
| 5.1015 | 0.9123 | 79500 | 5.4663 |
| 5.101 | 0.9181 | 80000 | 5.4672 |
| 5.1022 | 0.9238 | 80500 | 5.4654 |
| 5.1 | 0.9295 | 81000 | 5.4632 |
| 5.0981 | 0.9353 | 81500 | 5.4637 |
| 5.0981 | 0.9410 | 82000 | 5.4630 |
| 5.0951 | 0.9468 | 82500 | 5.4619 |
| 5.0941 | 0.9525 | 83000 | 5.4628 |
| 5.0947 | 0.9582 | 83500 | 5.4621 |
| 5.0949 | 0.9640 | 84000 | 5.4625 |
| 5.0971 | 0.9697 | 84500 | 5.4618 |
| 5.0895 | 0.9754 | 85000 | 5.4619 |
| 5.0937 | 0.9812 | 85500 | 5.4616 |
| 5.0978 | 0.9869 | 86000 | 5.4615 |
| 5.0958 | 0.9927 | 86500 | 5.4614 |
| 5.095 | 0.9984 | 87000 | 5.4614 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.2.0
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Text Generation
- Library
- transformers
- Type
- gpt2
- Created
- Jun 16, 2024
- Updated
- Jun 17, 2024