Instructions to use UF-NLPC-Lab/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UF-NLPC-Lab/test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="UF-NLPC-Lab/test_model", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UF-NLPC-Lab/test_model", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 649 Bytes
af4c621 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | from transformers.models.bert.configuration_bert import BertConfig
from typing import Optional
class BertForStanceConfig(BertConfig):
model_type = "bert_for_stance"
def __init__(self,
*,
classifier_hidden_units: Optional[int] = None,
**base_kwargs):
super().__init__(**base_kwargs)
self.problem_type = "single_label_classification"
self.add_pooling_layer = False
self.return_dict = True
self.classifier_hidden_units = classifier_hidden_units if classifier_hidden_units else self.hidden_size
BertForStanceConfig.register_for_auto_class("AutoConfig") |