--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:8963241 - loss:EmbedDistillLoss base_model: answerdotai/ModernBERT-large widget: - source_sentence: A man in shorts and a woman in a black and white polka dot bikini sunbathing on the beach. sentences: - A man is standing in a field with a green plant in it. - A woman preparing some pork. - uh-huh uh-huh well what other movies have you seen then lately - source_sentence: A man is standing in a forest practicing a martial art. sentences: - Two men are standing in a room, pointing in opposite directions, while another man is standing in between them looking to his left. - A woman in a white button up shirt is holding a rope and smiling. - And a special In Madrid, thanks to the siesta lunch break, the rush hour happens not twice, but three times a day. - source_sentence: A Boston Terrier is running on lush green grass in front of a white fence. sentences: - The distinctive, sculpted marble figures of the era are now being reproduced in vast quantities as souvenirs. - An older lady in a blue shirt in a rowboat. - A man wearing jeans and no shirt is midair doing a kick flip on a skateboard. - source_sentence: Two dogs are chasing a ball. sentences: - 'I loved you that first moment in the car when the bullet grazed your cheek… . Five minutes later Jane murmured softly: "I don''t know London very well, Julius, but is it such a very long way from the Savoy to the Ritz?"' - A person is sleeping on a bench, next to cars. - One of the more scenic is the five-mile River Mountain Trail, which offers fine views of both Lake Mead and the Las Vegas Valley. - source_sentence: A woman in purple riding a brown horse, competitively. sentences: - and uh they they have designated smoking but it's just wide open it's not ventilated properly and i think that's bad but as far as the drugs you know being in the factory kind of environment that way - 1.5, formerly methodology transfer paper 5. Using Statistical Sampling. - The oldest dances are survivors from Moorish times, and are usually performed in mountain villages. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on answerdotai/ModernBERT-large results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.8704018510417749 name: Pearson Cosine - type: spearman_cosine value: 0.8718236865761443 name: Spearman Cosine --- # SentenceTransformer based on answerdotai/ModernBERT-large This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Supported Modality:** Text ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'ModernBertModel'}) (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'mean', 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'A woman in purple riding a brown horse, competitively.', "and uh they they have designated smoking but it's just wide open it's not ventilated properly and i think that's bad but as far as the drugs you know being in the factory kind of environment that way", 'The oldest dances are survivors from Moorish times, and are usually performed in mountain villages.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[ 1.0000, -0.0255, 0.1637], # [-0.0255, 1.0000, 0.0579], # [ 0.1637, 0.0579, 1.0000]]) ```