The matching model for TESDA-TRACK. It is a fine-tune of intfloat/multilingual-e5-small that matches learner goals, written in English, Filipino or Taglish, to TESDA qualifications. The repo contains the ONNX model only, with no PyTorch weights. The TESDA-TRACK API runs it on CPU through fastembed. From the repository root, download the model into backend/models/: Add both lines to the root.env: Then embed the catalog with the new model, and restart the API: - Keep the name as given. EMBEDDINGMODEL must stay tesda-track/tesda-e5-small-v2, because stored embeddings are keyed by that name. Never set it to the base model's name. backend/models/ to /app/finetuned/, so set…
Independent publisher
Prince Esteves
Nyanta19
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Models on Hugging Face1
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