---
license: mit
language:
- fr
base_model:
- distilbert/distilbert-base-uncased
---
## Model Description

Named Entity Recognition (NER) model trained to identify specific entities in French text. The model is designed to recognize entities such as cities and train stations, which are useful for travel-related applications.

## Model Architecture

The model is based on the `AutoModelForTokenClassification` architecture from the Hugging Face Transformers library. It uses a pre-trained transformer model fine-tuned on a custom dataset for NER tasks.

## Training Data

The model was trained on a dataset of French train stations and cities. The data was preprocessed and converted into a format suitable for NER tasks. The training data includes various sentences with labeled entities.

## Labels

The model recognizes the following labels:
- `O`: Outside of a named entity
- `B-START`: Beginning of a start entity
- `I-START`: Inside of a start entity
- `B-END`: Beginning of an end entity
- `I-END`: Inside of an end entity

## Usage
You can use this model with the Hugging Face `transformers` library.