Sentence Similarity
Transformers
Safetensors
PEFT
sentence-transformers
English
lora
reinforcement-learning
domain-adaptation
sentence-embeddings
curriculum-learning
multi-task-learning
rag
information-retrieval
cross-domain
Eval Results (legacy)
Instructions to use EphAsad/DomainEmbedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EphAsad/DomainEmbedder with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("EphAsad/DomainEmbedder", dtype="auto") - PEFT
How to use EphAsad/DomainEmbedder with PEFT:
Task type is invalid.
- sentence-transformers
How to use EphAsad/DomainEmbedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("EphAsad/DomainEmbedder") 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
Update README.md
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README.md
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- information-retrieval
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- cross-domain
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- sentence-transformers
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base_model:
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pipeline_tag: sentence-similarity
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datasets:
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- sentence-transformers/stsb
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value: 0.925
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name: Training Accuracy
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- type: accuracy
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value: 0.
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name: Stress-Test Accuracy
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---
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## License
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MIT License
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- information-retrieval
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- cross-domain
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- sentence-transformers
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base_model:
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- sentence-transformers/all-MiniLM-L6-v2
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- EphAsad/FireDevourerEmbedder-RL-v3.6
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pipeline_tag: sentence-similarity
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datasets:
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- sentence-transformers/stsb
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value: 0.925
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name: Training Accuracy
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- type: accuracy
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value: 0.56
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name: Stress-Test Accuracy
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---
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## License
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MIT License
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