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README.md
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---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- transformers
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---
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#
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('
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model = AutoModel.from_pretrained('
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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Parameters of the fit()-Method:
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```
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{
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"epochs":
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"evaluation_steps": 0,
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"evaluator": "NoneType",
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"max_grad_norm": 1,
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---
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language:
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- dv
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- transformers
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---
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# Dhivehi TSDAE News BERT
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('ashraq/tsdae-bert-base-dv-news-title')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('ashraq/tsdae-bert-base-dv-news-title')
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model = AutoModel.from_pretrained('ashraq/tsdae-bert-base-dv-news-title')
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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Parameters of the fit()-Method:
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```
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{
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"epochs": 3,
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"evaluation_steps": 0,
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"evaluator": "NoneType",
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"max_grad_norm": 1,
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