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results
This model is a fine-tuned version of jjzha/jobbert-base-cased for the task of token classification. It achieves the following results on the evaluation set:
- Loss: 0.1244
- Accuracy: 0.9701
- Precision: 0.5581
- Recall: 0.6814
- F1: 0.6136
Model description
The base model (jjzha/jobbert-base-cased) is a BERT transformer model, pretrained on a corpus of ~3.2 million sentences from job adverts for the objective of Masked Language Modelling (MLM). A token classification head is added to the top of the model to predict a label for every token in a given sequence. In this instance, it is predicting a label for every token in a job description, where the label is either a 'B-SKILL', 'I-SKILL' or 'O' (not a skill).
Training and evaluation data
The model was trained on 4112 job advert sentences.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 257 | 0.0769 | 0.9725 | 0.5578 | 0.7003 | 0.6210 |
| 0.0816 | 2.0 | 514 | 0.1051 | 0.9653 | 0.5086 | 0.7445 | 0.6044 |
| 0.0816 | 3.0 | 771 | 0.0986 | 0.9709 | 0.5761 | 0.7161 | 0.6385 |
| 0.0262 | 4.0 | 1028 | 0.1140 | 0.9703 | 0.5627 | 0.6940 | 0.6215 |
| 0.0262 | 5.0 | 1285 | 0.1244 | 0.9701 | 0.5581 | 0.6814 | 0.6136 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
