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HBERTv1_emb_compress_48_L10_H64_A2
gokuls/HBERTv1_emb_compress_48_L10_H64_A2
HBERTv1_emb_compress_48_L10_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
- b47335b5
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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_L10_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: 6.4110
- Accuracy: 0.1281
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 |
|---|---|---|---|---|
| 8.6666 | 0.16 | 10000 | 8.5962 | 0.0465 |
| 7.2486 | 0.33 | 20000 | 7.2447 | 0.0467 |
| 7.0176 | 0.49 | 30000 | 7.0089 | 0.0670 |
| 6.8859 | 0.66 | 40000 | 6.8795 | 0.0840 |
| 6.7911 | 0.82 | 50000 | 6.7857 | 0.0918 |
| 6.7203 | 0.98 | 60000 | 6.7210 | 0.0952 |
| 6.6722 | 1.15 | 70000 | 6.6715 | 0.1004 |
| 6.6362 | 1.31 | 80000 | 6.6338 | 0.1033 |
| 6.5995 | 1.47 | 90000 | 6.6017 | 0.1065 |
| 6.5756 | 1.64 | 100000 | 6.5755 | 0.1092 |
| 6.5546 | 1.8 | 110000 | 6.5521 | 0.1118 |
| 6.5302 | 1.97 | 120000 | 6.5330 | 0.1138 |
| 6.5121 | 2.13 | 130000 | 6.5166 | 0.1156 |
| 6.5049 | 2.29 | 140000 | 6.5005 | 0.1174 |
| 6.483 | 2.46 | 150000 | 6.4869 | 0.1190 |
| 6.4757 | 2.62 | 160000 | 6.4755 | 0.1205 |
| 6.4659 | 2.79 | 170000 | 6.4655 | 0.1219 |
| 6.4527 | 2.95 | 180000 | 6.4569 | 0.1227 |
| 6.4517 | 3.11 | 190000 | 6.4477 | 0.1237 |
| 6.441 | 3.28 | 200000 | 6.4422 | 0.1245 |
| 6.4385 | 3.44 | 210000 | 6.4353 | 0.1253 |
| 6.4308 | 3.6 | 220000 | 6.4295 | 0.1256 |
| 6.4188 | 3.77 | 230000 | 6.4250 | 0.1264 |
| 6.422 | 3.93 | 240000 | 6.4213 | 0.1269 |
| 6.416 | 4.1 | 250000 | 6.4180 | 0.1273 |
| 6.4215 | 4.26 | 260000 | 6.4151 | 0.1276 |
| 6.4135 | 4.42 | 270000 | 6.4142 | 0.1277 |
| 6.4138 | 4.59 | 280000 | 6.4118 | 0.1281 |
| 6.41 | 4.75 | 290000 | 6.4097 | 0.1283 |
| 6.4114 | 4.92 | 300000 | 6.4103 | 0.1281 |
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