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HBERTv1_emb_compress_48_L12_H64_A2
gokuls/HBERTv1_emb_compress_48_L12_H64_A2
HBERTv1_emb_compress_48_L12_H64_A2 is a fill-mask model from gokuls. Use it when you need the model to fill a missing word. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Fill-Mask
- Library
- transformers
- Model type
- hybridbert
- Access
- Public
- Created
- Sep 27, 2023
- Updated
- Sep 28, 2023
- SHA
- 503d744e
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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. -->
HBERTv1_emb_compress_48_L12_H64_A2
This model is a fine-tuned version of on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 6.4079
- Accuracy: 0.1285
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: 1e-05
- train_batch_size: 96
- eval_batch_size: 96
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 8.6554 | 0.16 | 10000 | 8.5846 | 0.0483 |
| 7.2331 | 0.33 | 20000 | 7.2280 | 0.0542 |
| 7.0014 | 0.49 | 30000 | 6.9927 | 0.0677 |
| 6.8699 | 0.66 | 40000 | 6.8637 | 0.0856 |
| 6.7777 | 0.82 | 50000 | 6.7726 | 0.0922 |
| 6.7091 | 0.98 | 60000 | 6.7101 | 0.0974 |
| 6.6626 | 1.15 | 70000 | 6.6620 | 0.1015 |
| 6.6279 | 1.31 | 80000 | 6.6255 | 0.1040 |
| 6.5917 | 1.47 | 90000 | 6.5948 | 0.1068 |
| 6.5691 | 1.64 | 100000 | 6.5695 | 0.1094 |
| 6.5486 | 1.8 | 110000 | 6.5460 | 0.1122 |
| 6.5246 | 1.97 | 120000 | 6.5275 | 0.1144 |
| 6.5069 | 2.13 | 130000 | 6.5115 | 0.1162 |
| 6.5001 | 2.29 | 140000 | 6.4962 | 0.1180 |
| 6.4785 | 2.46 | 150000 | 6.4822 | 0.1197 |
| 6.4706 | 2.62 | 160000 | 6.4714 | 0.1212 |
| 6.4612 | 2.79 | 170000 | 6.4610 | 0.1225 |
| 6.4485 | 2.95 | 180000 | 6.4530 | 0.1233 |
| 6.4477 | 3.11 | 190000 | 6.4441 | 0.1243 |
| 6.4373 | 3.28 | 200000 | 6.4395 | 0.1251 |
| 6.4351 | 3.44 | 210000 | 6.4322 | 0.1259 |
| 6.4273 | 3.6 | 220000 | 6.4264 | 0.1262 |
| 6.4153 | 3.77 | 230000 | 6.4219 | 0.1269 |
| 6.4188 | 3.93 | 240000 | 6.4182 | 0.1274 |
| 6.4128 | 4.1 | 250000 | 6.4150 | 0.1278 |
| 6.4189 | 4.26 | 260000 | 6.4121 | 0.1280 |
| 6.4102 | 4.42 | 270000 | 6.4112 | 0.1282 |
| 6.4105 | 4.59 | 280000 | 6.4087 | 0.1285 |
| 6.4065 | 4.75 | 290000 | 6.4067 | 0.1287 |
| 6.4082 | 4.92 | 300000 | 6.4070 | 0.1285 |
Framework versions
- Transformers 4.33.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Fill-Mask
- Library
- transformers
- Type
- hybridbert
- Created
- Sep 27, 2023
- Updated
- Sep 28, 2023