Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use huiwonLee/LABSE_ft_employee_sts_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use huiwonLee/LABSE_ft_employee_sts_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("huiwonLee/LABSE_ft_employee_sts_v1") 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] - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length
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{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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Parameters of the fit()-Method:
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"epochs":
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"scheduler": "WarmupLinear",
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```
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 41 with parameters:
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{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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Parameters of the fit()-Method:
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{
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"epochs": 2,
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"evaluation_steps": 50,
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"evaluator": "NoneType",
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"max_grad_norm": 1,
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"scheduler": "WarmupLinear",
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"warmup_steps": 8,
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"weight_decay": 0.01
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```
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