Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:5817740
loss:MaskedCachedMultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use hanwenzhu/all-distilroberta-v1-lr2e-4-bs256-nneg3-ml-mar13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hanwenzhu/all-distilroberta-v1-lr2e-4-bs256-nneg3-ml-mar13 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hanwenzhu/all-distilroberta-v1-lr2e-4-bs256-nneg3-ml-mar13") sentences = [ "Mathlib.Data.Finset.Option#52", "neg_inj", "CategoryTheory.Limits.HasCokernels.has_colimit", "Finset.mem_image" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "/data/user_data/thomaszh/models/all-distilroberta-v1-lr2e-4-bs256-nneg3-ml/final", | |
| "architectures": [ | |
| "RobertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "gradient_checkpointing": false, | |
| "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": "roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.45.1", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 50265 | |
| } | |