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m2v_nomic-embed-text-v1.5_384

Model2Vec
Safetensors
sentence-transformers
English
embeddings
static-embeddings
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Update README.md

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README.md CHANGED
@@ -4,7 +4,7 @@ language:
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  - en
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  library_name: model2vec
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  license: mit
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- model_name: z_m2v_nomic-embed-text-v1.5_384
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  tags:
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  - embeddings
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  - static-embeddings
@@ -34,7 +34,7 @@ Load this model using the `from_pretrained` method:
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  from model2vec import StaticModel
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  # Load a pretrained Model2Vec model
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- model = StaticModel.from_pretrained("z_m2v_nomic-embed-text-v1.5_384")
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  # Compute text embeddings
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  embeddings = model.encode(["Example sentence"])
@@ -48,7 +48,7 @@ You can also use the [Sentence Transformers library](https://github.com/UKPLab/s
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  from sentence_transformers import SentenceTransformer
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  # Load a pretrained Sentence Transformer model
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- model = SentenceTransformer("z_m2v_nomic-embed-text-v1.5_384")
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  # Compute text embeddings
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  embeddings = model.encode(["Example sentence"])
 
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  - en
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  library_name: model2vec
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  license: mit
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+ model_name: clembi/m2v_nomic-embed-text-v1.5_384
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  tags:
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  - embeddings
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  - static-embeddings
 
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  from model2vec import StaticModel
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  # Load a pretrained Model2Vec model
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+ model = StaticModel.from_pretrained("clembi/m2v_nomic-embed-text-v1.5_384")
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  # Compute text embeddings
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  embeddings = model.encode(["Example sentence"])
 
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  from sentence_transformers import SentenceTransformer
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  # Load a pretrained Sentence Transformer model
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+ model = SentenceTransformer("clembi/m2v_nomic-embed-text-v1.5_384")
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  # Compute text embeddings
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  embeddings = model.encode(["Example sentence"])