File size: 2,706 Bytes
fcb5cb7 c5294aa fcb5cb7 7422e06 fcb5cb7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
---
pipeline_tag: token-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
The NER model developed using BERT is designed to recognize named entities in text for multiple languages, including Arabic, French, and English. It is adaptable to new labels, allowing users to extend its capabilities beyond the initial set of 10 predefined labels. which are: 'Person_Name', 'Brand_vehicule', 'Model_vehicule', 'Organization_Name', 'location', 'phone_number', 'IBAN', 'credit_card', 'date_time', 'email', 'Identification_Number'
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** yahya mdarhri
- **Model type:** TOKEN CLASSIFICATION
- **Finetuned from model :** bert-base-multilingual-cased
- **License:** OPEN SOURCE
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
Named Entity Recognition (NER): The primary purpose of this model is to perform Named Entity Recognition (NER) in text data. It identifies and categorizes entities such as names of people, organizations, locations, dates, and more.
Multilingual Support: The model is designed to support multiple languages, including Arabic, French, and English. It can be used by NLP practitioners, researchers, and developers working with text data in these languages.
Adaptability: Users can adapt the model to recognize new entity labels by providing labeled training data for the desired categories. This feature makes it versatile for various NER tasks.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Bias and Fairness: Users and affected parties should be aware of potential biases in entity recognition, especially when it comes to personal names or other sensitive categories. Efforts should be made to minimize bias and ensure fairness in entity recognition.
Privacy: The model should be used responsibly to protect the privacy of individuals and organizations. When handling personally identifiable information (PII), data protection laws and privacy guidelines should be followed.
Transparency: Transparency in how the model operates, including its training data and evaluation metrics, is crucial to build trust with users and affected parties.
User Consent: If the model is used in applications where user data is processed, obtaining informed consent from users for data processing is essential.
We value your feedback! Please share your thoughts on this model. Thank you!
## Model Card Contact
yahyamdarhri00@gmail.com
|