| --- |
| base_model: jjzha/jobbert-base-cased |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: results |
| results: [] |
| widget: |
| - text: You should be a skilled communicator. |
| - text: You can programme in Python and CSS. |
| --- |
| |
| <!-- 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. --> |
|
|
| # results |
|
|
| This model is a fine-tuned version of [jjzha/jobbert-base-cased](https://huggingface.co/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 |
|
|
