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--- |
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base_model: jjzha/jobbert-base-cased |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: results |
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results: [] |
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widget: |
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- text: You should be a skilled communicator. |
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- text: You can programme in Python and CSS. |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# results |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1244 |
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- Accuracy: 0.9701 |
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- Precision: 0.5581 |
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- Recall: 0.6814 |
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- F1: 0.6136 |
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## Model description |
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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). |
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## Training and evaluation data |
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The model was trained on 4112 job advert sentences. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 1.0 | 257 | 0.0769 | 0.9725 | 0.5578 | 0.7003 | 0.6210 | |
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| 0.0816 | 2.0 | 514 | 0.1051 | 0.9653 | 0.5086 | 0.7445 | 0.6044 | |
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| 0.0816 | 3.0 | 771 | 0.0986 | 0.9709 | 0.5761 | 0.7161 | 0.6385 | |
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| 0.0262 | 4.0 | 1028 | 0.1140 | 0.9703 | 0.5627 | 0.6940 | 0.6215 | |
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| 0.0262 | 5.0 | 1285 | 0.1244 | 0.9701 | 0.5581 | 0.6814 | 0.6136 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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