Fill-Mask
Transformers
Safetensors
PyTorch
English
saute
feature-extraction
masked-language-modeling
dialogue
speaker-aware
transformer
custom_code
Instructions to use JustinDuc/saute with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JustinDuc/saute with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JustinDuc/saute", trust_remote_code=True, device_map="auto")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JustinDuc/saute", trust_remote_code=True, dtype="auto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "UtteranceEmbedings" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "auto_map": { | |
| "AutoConfig": "saute_config.SAUTEConfig", | |
| "AutoModel": "saute_model.UtteranceEmbedings" | |
| }, | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "intermediate_size": 3072, | |
| "max_edu_length": 128, | |
| "max_edus_per_dialog": 100, | |
| "max_position_embeddings": 512, | |
| "max_speakers": 200, | |
| "model_type": "saute", | |
| "num_attention_heads": 8, | |
| "num_edu_layers": 2, | |
| "num_hidden_layers": 3, | |
| "num_speaker_embeddings": 512, | |
| "num_token_layers": 2, | |
| "speaker_embeddings_size": 768, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.52.4", | |
| "vocab_size": 30522 | |
| } | |