Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
mpnet
fill-mask
feature-extraction
text-embeddings-inference
Eval Results
Instructions to use sentence-transformers/all-mpnet-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/all-mpnet-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2") 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 sentence-transformers/all-mpnet-base-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2") model = AutoModelForMaskedLM.from_pretrained("sentence-transformers/all-mpnet-base-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
Change max_position_embeddings to 512
#21
by vkehfdl1 - opened
- config.json +1 -1
config.json
CHANGED
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@@ -12,7 +12,7 @@
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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-
"max_position_embeddings":
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"model_type": "mpnet",
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"num_attention_heads": 12,
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| 18 |
"num_hidden_layers": 12,
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| 12 |
"initializer_range": 0.02,
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| 13 |
"intermediate_size": 3072,
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| 14 |
"layer_norm_eps": 1e-05,
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| 15 |
+
"max_position_embeddings": 512,
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| 16 |
"model_type": "mpnet",
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| 17 |
"num_attention_heads": 12,
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| 18 |
"num_hidden_layers": 12,
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