distilbert-base-uncased_fine_tuned_title
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2615
- Accuracy: {'accuracy': 0.877634820695319}
- Recall: {'recall': 0.8474786132372805}
- Precision: {'precision': 0.8953502200023784}
- F1: {'f1': 0.8707569536806801}
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
|---|---|---|---|---|---|---|---|
| 0.3093 | 1.0 | 2284 | 0.3021 | {'accuracy': 0.8779085683000274} | {'recall': 0.8560333183250788} | {'precision': 0.8888499298737728} | {'f1': 0.8721330275229358} |
| 0.2459 | 2.0 | 4568 | 0.2909 | {'accuracy': 0.8894059676977827} | {'recall': 0.8513057181449797} | {'precision': 0.9153957879448076} | {'f1': 0.8821882654846612} |
| 0.1696 | 3.0 | 6852 | 0.3259 | {'accuracy': 0.8808102929099371} | {'recall': 0.8595227375056281} | {'precision': 0.8915353181552831} | {'f1': 0.875236403232277} |
| 0.1179 | 4.0 | 9136 | 0.4946 | {'accuracy': 0.8729811114152751} | {'recall': 0.8610986042323278} | {'precision': 0.8756868131868132} | {'f1': 0.8683314415437005} |
| 0.0775 | 5.0 | 11420 | 0.6547 | {'accuracy': 0.8708458800985491} | {'recall': 0.8041422782530392} | {'precision': 0.9202627850057967} | {'f1': 0.8582927854868745} |
| 0.0522 | 6.0 | 13704 | 0.6699 | {'accuracy': 0.8768683274021353} | {'recall': 0.8325078793336335} | {'precision': 0.9067058967757754} | {'f1': 0.8680241769849187} |
| 0.0406 | 7.0 | 15988 | 0.8149 | {'accuracy': 0.8739118532712838} | {'recall': 0.8330706888788834} | {'precision': 0.9002554433767181} | {'f1': 0.8653610055539316} |
| 0.0298 | 8.0 | 18272 | 0.8906 | {'accuracy': 0.8753353408157679} | {'recall': 0.8421882035119316} | {'precision': 0.8952973555103506} | {'f1': 0.8679310944840787} |
| 0.0217 | 9.0 | 20556 | 1.0192 | {'accuracy': 0.8754448398576512} | {'recall': 0.8624493471409275} | {'precision': 0.8791738382099827} | {'f1': 0.8707312915506562} |
| 0.017 | 10.0 | 22840 | 1.0550 | {'accuracy': 0.8758828360251848} | {'recall': 0.8556956325979289} | {'precision': 0.8852917200419238} | {'f1': 0.8702421155056951} |
| 0.0139 | 11.0 | 25124 | 1.0873 | {'accuracy': 0.8728716123733917} | {'recall': 0.8582845565060784} | {'precision': 0.8776473296500921} | {'f1': 0.8678579558388345} |
| 0.0114 | 12.0 | 27408 | 1.1506 | {'accuracy': 0.8716123733917328} | {'recall': 0.8628995947771274} | {'precision': 0.8718298646650745} | {'f1': 0.8673417435085139} |
| 0.0061 | 13.0 | 29692 | 1.2574 | {'accuracy': 0.8696961401587736} | {'recall': 0.874943719045475} | {'precision': 0.8596549435965495} | {'f1': 0.8672319535869686} |
| 0.0035 | 14.0 | 31976 | 1.2490 | {'accuracy': 0.8784560635094443} | {'recall': 0.85006753714543} | {'precision': 0.8947867298578199} | {'f1': 0.8718540752713001} |
| 0.0028 | 15.0 | 34260 | 1.2615 | {'accuracy': 0.877634820695319} | {'recall': 0.8474786132372805} | {'precision': 0.8953502200023784} | {'f1': 0.8707569536806801} |
Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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