Feature Extraction
sentence-transformers
PyTorch
Dutch
roberta
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:483497
loss:SpladeLoss
loss:SparseMarginMSELoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use sparse-encoder/splade-robbert-dutch-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sparse-encoder/splade-robbert-dutch-base-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sparse-encoder/splade-robbert-dutch-base-v1") 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] - Notebooks
- Google Colab
- Kaggle
| { | |
| "additional_special_tokens_ids": [], | |
| "architectures": [ | |
| "RobertaForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "cls_token_id": 0, | |
| "eos_token_id": 3, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-05, | |
| "mask_token_id": 4, | |
| "max_position_embeddings": 514, | |
| "model_type": "roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "sep_token_id": 3, | |
| "tokenizer_class": "RobertaTokenizerFast", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.52.4", | |
| "type_vocab_size": 1, | |
| "unk_token_id": 2, | |
| "use_cache": true, | |
| "vocab_size": 50000 | |
| } | |