Text Generation
PEFT
Chinese
English
preference-learning
qlora
agent
personalization
association-engine
Instructions to use feiertu/hermes-association-engine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use feiertu/hermes-association-engine with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
| """测试 embedder 模块.""" | |
| import pytest | |
| from hermes_core.embedder import Embedder | |
| pytestmark = pytest.mark.requires_embedder | |
| class TestEmbedder: | |
| def embedder(self): | |
| return Embedder() | |
| def test_encode_returns_384_dim_vector(self, embedder): | |
| vec = embedder.encode("后端API开发") | |
| assert isinstance(vec, list) | |
| assert len(vec) == 384 | |
| assert all(isinstance(v, float) for v in vec) | |
| def test_encode_batch_returns_same_count(self, embedder): | |
| texts = ["后端开发", "前端开发", "游戏开发"] | |
| vecs = embedder.encode_batch(texts) | |
| assert len(vecs) == 3 | |
| assert all(len(v) == 384 for v in vecs) | |
| def test_similar_texts_have_high_similarity(self, embedder): | |
| a = embedder.encode("后端API开发") | |
| b = embedder.encode("后端服务开发") | |
| sim = embedder.cosine_similarity(a, b) | |
| assert sim > 0.5 | |
| def test_different_texts_have_low_similarity(self, embedder): | |
| a = embedder.encode("后端API开发") | |
| b = embedder.encode("周末去哪玩") | |
| sim = embedder.cosine_similarity(a, b) | |
| assert sim < 0.5 | |
| def test_identical_texts_have_similarity_one(self, embedder): | |
| a = embedder.encode("TypeScript") | |
| b = embedder.encode("TypeScript") | |
| sim = embedder.cosine_similarity(a, b) | |
| assert sim > 0.99 | |