embedingHF
/

Sentence_Transformer

Sentence Similarity

sentence-transformers

feature-extraction

text-embeddings-inference

Model card Files Files and versions

embedingHF commited on 14 days ago

Commit

9f39f04

·

verified ·

1 Parent(s): 98dbc5b

Update README.md

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -33,7 +33,7 @@ pipeline_tag: sentence-similarity
 ---
-# Detector_model
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 ## Usage (Sentence-Transformers)
@@ -48,7 +48,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
-model = SentenceTransformer('embedingHF/Detector_model')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -72,8 +72,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('embedingHF/Detector_model')
-model = AutoModel.from_pretrained('embedingHF/Detector_model')
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

 ---
+# Sentence_Transformer
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 ## Usage (Sentence-Transformers)
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
+model = SentenceTransformer('embedingHF/sentence_Tranformer')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 sentences = ['This is an example sentence', 'Each sentence is converted']
 # Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('embedingHF/Sentence_Transformer')
+model = AutoModel.from_pretrained('embedingHF/Sentence_Transformer')
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')