Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use thtang/ALL_679283 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thtang/ALL_679283 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thtang/ALL_679283") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use thtang/ALL_679283 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("thtang/ALL_679283") model = AutoModel.from_pretrained("thtang/ALL_679283") - Notebooks
- Google Colab
- Kaggle
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README.md
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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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| [thtang_ALL_679283](https://huggingface.co/thtang/ALL_679283) | 66.39 | 72.37 | 61.8 | 56.94 | 65.27 | 69.71 | 69.21 | 69.44 |
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| thtang_ALL_660924 | 66.44 | 72.63 | 61.74 | 57.22 | 65.44 | 69.77 | 69.06 | 69.23 |
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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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| Model | Avg | id_raw_acc | vn_raw_acc | br_raw_acc | th_raw_acc | my_raw_acc | ph_raw_acc | sg_raw_acc |
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| [thtang_ALL_679283](https://huggingface.co/thtang/ALL_679283) | 66.39 | 72.37 | 61.8 | 56.94 | 65.27 | 69.71 | 69.21 | 69.44 |
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| thtang_ALL_660924 | 66.44 | 72.63 | 61.74 | 57.22 | 65.44 | 69.77 | 69.06 | 69.23 |
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