Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use BlueAvenir/sti_modern_workplace_class_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BlueAvenir/sti_modern_workplace_class_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BlueAvenir/sti_modern_workplace_class_model") 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 BlueAvenir/sti_modern_workplace_class_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BlueAvenir/sti_modern_workplace_class_model") model = AutoModel.from_pretrained("BlueAvenir/sti_modern_workplace_class_model") - Notebooks
- Google Colab
- Kaggle
Commit ·
cda618f
1
Parent(s): bb94bd5
Add model
Browse files- README.md +3 -3
- model_head.pkl +1 -1
- pytorch_model.bin +1 -1
README.md
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**DataLoader**:
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{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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"lr": 2e-05
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```
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`torch.utils.data.dataloader.DataLoader` of length 300 with parameters:
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```
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{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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"lr": 2e-05
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": 300,
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"warmup_steps": 30,
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"weight_decay": 0.01
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}
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```
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model_head.pkl
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