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Model Card for ICILS XLM-R ISCO
This model is a fine-tuned version of ESCOXLM-R trained on The ICILS Multilingual ISCO-08 Parental Occupation Corpus.
A R&D report explaining the research is available at https://www.iea.nl/publications/rd-outcomes/improving-parental-occupation-coding-procedures-ai.
It achieves the following results on the test split:
- Loss: 1.7849
- Accuracy: 0.6285
- Hierarchical Accuracy: 0.95
The research paper, ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain, states "ESCOXLM-R, based on XLM-R-large, uses domain-adaptive pre-training on the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy, covering 27 languages. The pre-training objectives for ESCOXLM-R include dynamic masked language modeling and a novel additional objective for inducing multilingual taxonomical ESCO relations" (Zhang et al., ACL 2023).
Model Details
Model Description
IEA is an international cooperative of national research institutions, governmental research agencies, scholars, and analysts working to research, understand, and improve education worldwide.
- Developed by: The International Computer and Information Literacy Study
- Funded by: IEA International Association for the Evaluation of Educational Achievement
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model: ESCOXLM-R
Model Sources
- Repository: [More Information Needed]
- Paper: Improving parental occupation coding procedures AI
- Demo: https://huggingface.co/spaces/ICILS/ICILS-XLM-R-ISCO
Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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: 12.0
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 3.2269 | 1.0 | 3518 | 0.4176 | 2.9434 |
| 2.2851 | 2.0 | 7036 | 0.5250 | 2.2479 |
| 1.937 | 3.0 | 10554 | 0.5691 | 1.9822 |
| 1.4695 | 4.0 | 14072 | 0.6018 | 1.8560 |
| 1.2157 | 5.0 | 17590 | 0.6114 | 1.8160 |
| 0.9819 | 6.0 | 21108 | 0.6214 | 1.7946 |
| 0.8608 | 7.0 | 24626 | 0.6285 | 1.7849 |
| 0.8374 | 8.0 | 28144 | 0.6353 | 1.7893 |
| 0.7908 | 9.0 | 31662 | 1.8279 | 0.6239 |
| 0.6962 | 10.0 | 35180 | 1.8472 | 0.6347 |
| 0.6371 | 11.0 | 38698 | 1.8669 | 0.6339 |
| 0.5226 | 12.0 | 42216 | 1.8695 | 0.6336 |
Evaluation
Testing Data, Factors & Metrics
Testing Data
The model was trained on the icils configuration of the ISCO-08 dataset using the train and validation splits and evaluated on the test split.
Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
Citation [optional]
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Model tree for ICILS/xlm-r-icils-ilo
Base model
jjzha/esco-xlm-roberta-largeSpace using ICILS/xlm-r-icils-ilo 1
Evaluation results
- Accuracy on ICILS/multilingual_parental_occupationstest set self-reported0.628
- ISCO Hierarchical Accuracy on ICILS/multilingual_parental_occupationstest set self-reported0.950