Sentence Similarity
sentence-transformers
Safetensors
Transformers
bidirlm
feature-extraction
mteb
embedding
bidirectional
custom_code
Instructions to use BidirLM/BidirLM-1.7B-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BidirLM/BidirLM-1.7B-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BidirLM/BidirLM-1.7B-Embedding", trust_remote_code=True) 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] - Transformers
How to use BidirLM/BidirLM-1.7B-Embedding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BidirLM/BidirLM-1.7B-Embedding", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BidirLMModel" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_bidirlm.BidirLMConfig", | |
| "AutoModel": "modeling_bidirlm.BidirLMModel", | |
| "AutoModelForMaskedLM": "modeling_bidirlm.BidirLMForMaskedLM", | |
| "AutoModelForPreTraining": "modeling_bidirlm.BidirLMPreTrainedModel", | |
| "AutoModelForSequenceClassification": "modeling_bidirlm.BidirLMForSequenceClassification", | |
| "AutoModelForTokenClassification": "modeling_bidirlm.BidirLMForTokenClassification" | |
| }, | |
| "bos_token_id": 151644, | |
| "clf_pooling": "late", | |
| "dtype": "bfloat16", | |
| "eos_token_id": 151645, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 6144, | |
| "mask_token": "<|mask|>", | |
| "mask_token_id": 151663, | |
| "max_position_embeddings": 32768, | |
| "max_window_layers": 28, | |
| "model_type": "bidirlm", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 28, | |
| "num_key_value_heads": 8, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 1000000, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "4.57.6", | |
| "vocab_size": 151936 | |
| } | |