| language: en | |
| tags: | |
| - ner | |
| - pytorch | |
| license: mit | |
| # CNN for Named Entity Recognition | |
| This model is a CNN-based model for Named Entity Recognition (NER) built on top of a pre-trained transformer model. | |
| ## Model description | |
| The model uses a pre-trained transformer as a base and adds convolutional layers on top for NER tasks. | |
| ## Intended uses & limitations | |
| This model is intended for Named Entity Recognition tasks. It should be used on Yoruba text data. | |
| ## Usage | |
| To use this model: | |
| ```python | |
| from transformers import AutoTokenizer | |
| from custom_modeling import get_model | |
| tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-name") | |
| model = get_model("your-username/your-model-name") | |
| # Load the saved weights | |
| import torch | |
| model.load_state_dict(torch.load("pytorch_model.bin")) | |
| # Use the model for inference | |
| inputs = tokenizer("Your text here", return_tensors="pt") | |
| outputs = model(**inputs) | |
| ``` | |