Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:53851
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use danthepol/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danthepol/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danthepol/MNLP_M3_document_encoder") sentences = [ "A certain junior class has 1000 students and a certain senior class has 900 students. Among these students, there are 60 siblings pairs each consisting of 1 junior and 1 senior. If 1 student is to be selected at random from each class, what is the probability that the 2 students selected will be a sibling pair?", "Let's see Pick 60/1000 first Then we can only pick 1 other pair from the 800 So total will be 60 / 900 *1000 Simplify and you get 2/30000", "To maximize number of hot dogs with 300$ Total number of hot dogs bought in 250-pack = 22.95*13 =298.35$ Amount remaining = 300 - 298.35 = 1.65$ This amount is too less to buy any 8- pack . Greatest number of hot dogs one can buy with 300 $ = 250*13 = 3250", "artificial leg" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "LABEL_0" | |
| }, | |
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| "intermediate_size": 3072, | |
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| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.51.3", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |