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quangtqv/
bi_encoder_tool_learning_9_5_2024_v6

Name: bi_encoder_tool_learning_9_5_2024_v6
Availability: InStock

sentence-transformers

text-embeddings-inference

endpoints_compatible

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quangtqv/bi_encoder_tool_learning_9_5_2024_v6

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

python

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('quangtqv/bi_encoder_tool_learning_9_5_2024_v6')
embeddings = model.encode(sentences)
print(embeddings)

Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

quangtqv/bi_encoder_tool_learning_9_5_2024_v6

quangtqv/bi_encoder_tool_learning_9_5_2024_v6

Usage (Sentence-Transformers)

Evaluation Results

Full Model Architecture

Citing & Authors

quangtqv/
bi_encoder_tool_learning_9_5_2024_v6