Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
bert
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Jenjamin3000/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Jenjamin3000/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Jenjamin3000/MNLP_M2_document_encoder") 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 Jenjamin3000/MNLP_M2_document_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Jenjamin3000/MNLP_M2_document_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Jenjamin3000/MNLP_M2_document_encoder") model = AutoModel.from_pretrained("Jenjamin3000/MNLP_M2_document_encoder") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -1
config.json
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"LABEL_0": 0
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"layer_norm_eps": 1e-12,
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"max_position_embeddings":
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"model_type": "bert",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"LABEL_0": 0
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 603,
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"model_type": "bert",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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