tok_train_info
This model is a fine-tuned version of jjzha/jobbert_knowledge_extraction on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2616
- Precision: 0.5755
- Recall: 0.5980
- F1: 0.5865
- Accuracy: 0.9072
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 20 | 0.4390 | 0.3790 | 0.4608 | 0.4159 | 0.8845 |
| No log | 2.0 | 40 | 0.2831 | 0.5321 | 0.5686 | 0.5498 | 0.9034 |
| No log | 3.0 | 60 | 0.2616 | 0.5755 | 0.5980 | 0.5865 | 0.9072 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Base model
jjzha/jobbert_knowledge_extraction