Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
modernbert
feature-extraction
embeddings
Eval Results (legacy)
text-embeddings-inference
Instructions to use mjbommar/ogbert-2m-sentence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mjbommar/ogbert-2m-sentence with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mjbommar/ogbert-2m-sentence") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use mjbommar/ogbert-2m-sentence with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mjbommar/ogbert-2m-sentence") model = AutoModel.from_pretrained("mjbommar/ogbert-2m-sentence") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "mjbommar/ogbert-2m-sentence", | |
| "architectures": [ | |
| "ModernBertModel" | |
| ], | |
| "model_type": "modernbert", | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 0, | |
| "cls_token_id": 4, | |
| "eos_token_id": 1, | |
| "sep_token_id": 5, | |
| "pad_token_id": 2, | |
| "unk_token_id": 3, | |
| "mask_token_id": 6, | |
| "classifier_activation": "gelu", | |
| "classifier_bias": false, | |
| "classifier_dropout": 0.0, | |
| "classifier_pooling": "cls", | |
| "decoder_bias": true, | |
| "deterministic_flash_attn": false, | |
| "dtype": "float32", | |
| "embedding_dropout": 0.0, | |
| "global_attn_every_n_layers": 3, | |
| "hidden_act": "gelu", | |
| "hidden_activation": "gelu", | |
| "hidden_size": 128, | |
| "initializer_cutoff_factor": 2.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 512, | |
| "layer_norm_eps": 1e-05, | |
| "layer_types": [ | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention" | |
| ], | |
| "local_attention": 128, | |
| "max_position_embeddings": 1024, | |
| "mlp_bias": false, | |
| "mlp_dropout": 0.0, | |
| "norm_bias": false, | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 4, | |
| "repad_logits_with_grad": false, | |
| "rope_parameters": { | |
| "full_attention": { | |
| "rope_theta": 160000.0, | |
| "rope_type": "default" | |
| }, | |
| "sliding_attention": { | |
| "rope_theta": 10000.0, | |
| "rope_type": "default" | |
| } | |
| }, | |
| "sparse_pred_ignore_index": -100, | |
| "sparse_prediction": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.47.0", | |
| "vocab_size": 8192 | |
| } | |