---
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.
---

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# 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