quangtqv / bi_encoder_tool_learning_14_5_2024

huggingface.co
Total runs: 9
24-hour runs: 0
7-day runs: -2
30-day runs: -9
Model's Last Updated: May 14 2024
sentence-similarity

Introduction of bi_encoder_tool_learning_14_5_2024

Model Details of bi_encoder_tool_learning_14_5_2024

quangtqv/bi_encoder_tool_learning_14_5_2024

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:

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

model = SentenceTransformer('quangtqv/bi_encoder_tool_learning_14_5_2024')
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()
)
Citing & Authors

Runs of quangtqv bi_encoder_tool_learning_14_5_2024 on huggingface.co

9
Total runs
0
24-hour runs
-3
3-day runs
-2
7-day runs
-9
30-day runs

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bi_encoder_tool_learning_14_5_2024 install

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