Instructions to use ta012/SSLAM_AS2M_Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ta012/SSLAM_AS2M_Finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ta012/SSLAM_AS2M_Finetuned", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ta012/SSLAM_AS2M_Finetuned", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # modeling_eat.py | |
| from transformers import PreTrainedModel | |
| from .configuration_eat import EATConfig | |
| from .eat_model import EAT | |
| class EATModel(PreTrainedModel): | |
| config_class = EATConfig | |
| def __init__(self, config: EATConfig): | |
| super().__init__(config) | |
| self.model = EAT(config) | |
| def forward(self, *args, **kwargs): | |
| return self.model(*args, **kwargs) | |
| def extract_features(self, x): | |
| return self.model.extract_features(x) | |