81 lines
1.9 KiB
Markdown
81 lines
1.9 KiB
Markdown
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---
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language:
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- en
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base_model:
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- microsoft/phi-4
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- phi
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- fine-tuned
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- full-finetune
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- instruction-tuning
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- text-generation
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- recruitment
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- resume-parsing
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- job-description-generation
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---
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# IMCatalina-v1.0
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## Model summary
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**IMCatalina-v1.0** is a **fully fine-tuned** version of **Phi-4** specialized in **recruitment document processing**.
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The model focuses exclusively on:
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- Parsing unstructured CVs/resumes
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- Converting CV content into structured formats (JSON / YAML)
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- Generating professional job descriptions from structured inputs
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This model was trained end-to-end (full fine-tuning) and **does not perform candidate scoring, ranking, or hiring decisions**.
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---
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## Intended use
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### Primary use cases
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- CV and resume parsing
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- Structured CV normalization (JSON / YAML)
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- Extraction of skills, roles, education, and experience
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- Job description generation for recruitment platforms
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- Preprocessing for ATS and HR systems
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### Explicitly out-of-scope
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- Candidate ranking or scoring
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- Hiring recommendations
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- Candidate–job matching
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- Automated decision-making
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- Psychological or behavioral inference
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---
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## Model details
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- **Base model:** microsoft/phi-4
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- **Model type:** Decoder-only causal language model
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- **Architecture:** Transformer (Phi family)
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- **Parameters:** ~14B
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- **Context length:** up to 16k tokens
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- **Languages:** English
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- **Training type:** Full fine-tuning
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---
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## Training
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### Training data
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- **Domain:** Recruitment and HR documentation
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- **Data type:** Synthetic and curated structured data
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- **Formats:**
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- Instruction–response
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- Schema-constrained generation
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- **Content includes:**
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- CVs and resumes
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- Job descriptions
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- Skills, roles, education, and experience fields
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- **Data processing:**
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- Deduplication
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- Schema validation
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- Removal of malformed samples
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- Consistency and format checks
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> No real personal data was intentionally included in the training datasets.
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