Model Card for Military Experience Classification Model
This modelcard describes a binary classification model for identifying military experience in text, specifically for messages from Telegram channels.
Model Details
Model Description
This model is designed to classify whether a given text contains references to military experience. It was trained on a custom dataset, consisting of messages from Telegram channels.
- Developed by: bodomerka
- Language(s) (NLP): Ukrainian, Russian
- License: Apache-2.0
- Model type: Binary classification
- Finetuned from model: ai-forever/ruBert-base
Model Sources
- Repository: [More Information Needed]
- Paper: [More Information Needed]
- Demo: [More Information Needed]
Uses
Direct Use
This model can be used to classify whether a given text contains military experience, providing a binary output.
Downstream Use
The model can be incorporated into applications that need to identify military experience in text, such as job matching systems or resume parsers.
Out-of-Scope Use
The model may not perform well on texts outside the domain of military experience, such as completely unrelated subjects or specialized military terms not included in the training dataset.
Bias, Risks, and Limitations
- The model may not perform well if the input text contains incomplete sentences or non-meaningful content.
- Specialized military terminology not included in the training data could result in misclassifications.
Recommendations
Users should be aware of the model's limitations and risks, especially when handling non-meaningful or highly specialized texts.
How to Get Started with the Model
To use the model, you can load it directly with the Hugging Face transformers library using the following code:
1from transformers import pipeline
2
3model = "bodomerka/military-experience-classification"
4classifier = pipeline("text-classification", model=model)
5
6text = "This is an example text mentioning military experience."
7result = classifier(text)
8print(result)
