Instructions to use fhamborg/newsframes-aff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use fhamborg/newsframes-aff with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fhamborg/newsframes-aff") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - setfit
How to use fhamborg/newsframes-aff with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("fhamborg/newsframes-aff") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "models/sentence-transformers-all-mpnet-base-v2-dataset_systematic-random_compare_with-absent_radius-1.json-AFF_4-False-0.15-5e-06-20-4-0.8043882339240336-None-False/", | |
| "architectures": [ | |
| "MPNetModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "mpnet", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "relative_attention_num_buckets": 32, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.32.1", | |
| "vocab_size": 30527 | |
| } | |