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--- |
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license: mit |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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--- |
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# liuxiong332/bge-m3-financial-mixed |
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. |
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Base on BAAI/bge-m3, finetuned with financial data |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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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('liuxiong332/bge-m3-financial-mixed') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Evaluation Results |
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) |
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## Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) |
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(2): Normalize() |
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) |
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``` |
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