Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
politics
agenda
issues
comparative agendas project
political communication
bills
laws
parliament
text-embeddings-inference
Instructions to use z-dickson/CAP_coded_US_Congressional_bills with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use z-dickson/CAP_coded_US_Congressional_bills with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="z-dickson/CAP_coded_US_Congressional_bills")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("z-dickson/CAP_coded_US_Congressional_bills") model = AutoModelForSequenceClassification.from_pretrained("z-dickson/CAP_coded_US_Congressional_bills") - Notebooks
- Google Colab
- Kaggle
| {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "bert-base-cased", "tokenizer_class": "BertTokenizer"} |