Sentence Similarity
Transformers
PyTorch
Chinese
bert
feature-extraction
PEG
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use TownsWu/PEG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TownsWu/PEG with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TownsWu/PEG") model = AutoModel.from_pretrained("TownsWu/PEG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -46,4 +46,5 @@ with torch.no_grad():
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last_hidden_state = model(**inputs, return_dict=True).last_hidden_state
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embeddings = last_hidden_state[:, 0]
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print("embeddings:")
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print(embeddings)
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last_hidden_state = model(**inputs, return_dict=True).last_hidden_state
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embeddings = last_hidden_state[:, 0]
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print("embeddings:")
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print(embeddings)
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```
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