Model Card for Model ID
Model Details
Model Description
This entity recognition model is used for information extraction in job-related texts. It can identify entities of Occupation, Skills, Qualifications, Experience and Domain. It is trained based on the open-source dataset provided by Green et. al.
- Developed by: International Hellenic University NLP
- Model type: Token Classification
- Language(s) (NLP): English
- Finetuned from model: FacebookAI/roberta-base
Uses
Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("tabiya/roberta-base-job-ner")
model = AutoModelForTokenClassification.from_pretrained("tabiya/roberta-base-job-ner")
Training Details
Training Data
More information about the training dataset can be found here
Training Procedure
The training of this model was done using the HuggingFace token classification tutorial
Training Hyperparameters
- Max Length: 128
- Batch Size: 32
- Learning Rate: 0.0001
- Epochs: 5
- Weight Decay: 0.01
Evaluation
Testing Data, Factors & Metrics
Testing Data
The training was evaluated on the test set of the training dataset provided by Green et. al.
Metrics
Following standard procedures for evaluating entity recognition training the chosen metric was the strict span-F1 measure provided by the seqeval library.
Results
| Entity Category | Strict Span-F1 |
|---|---|
| Domain | 0.3 |
| Experience | 0.56 |
| Occuption | 0.83 |
| Qualification | 0.55 |
| Skill | 0.51 |
| Micro Average | 0.56 |
Compute Infrastructure
The hyperparameter search and training was done on the Advanced Research Computing (ARC) of the University of Oxford.
Hardware
All training was performed on V100 GPUs.
Citation
BibTeX:
TBD