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Selenaydmrp/
flan-t5-base-turkish-ott-query-parser

Name: flan-t5-base-turkish-ott-query-parser
Rating: 2.5 (7 reviews)

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FLAN-T5 Base Turkish OTT Query Parser

This model is a fine-tuned version of google/flan-t5-base for Turkish OTT and media search query parsing.

The model takes a Turkish natural language media search query as input and generates a structured JSON output. Its purpose is to convert user queries into machine-readable filters that can be used in search, recommendation, filtering, or vector database systems.

What This Model Does

This model is designed to understand Turkish OTT/media search queries.

It can extract information such as:

genres
excluded genres
names
country filters
language filters
mood or theme tags
title or channel names
similar title requests
rating and popularity filters
live broadcast intent

For example, the query:

yerli dram dizileri olsun ama romantik olmasın

can be converted into:

json

{
  "intent_type": "content_search",
  "filters": {
    "country_names": ["Türkiye"],
    "genres": ["dram"],
    "exclude_genres": ["romantik"]
  }
}

Intended Use

This model can be used as a query understanding layer in Turkish OTT/media search systems.

A typical usage flow is:

text

User Query
→ Query Parser Model
→ Structured JSON Filters
→ Search / Filtering / Vector Database
→ Final Results

The model does not directly search for movies, series, or channels. It only extracts structured filters from the user query.

Example Inputs and Outputs

Example 1

Input:

yerli komedi filmleri

Output:

json

{
  "intent_type": "content_search",
  "filters": {
    "country_names": ["Türkiye"],
    "genres": ["komedi"]
  }
}

Example 2

Input:

trt 1 canlı yayın

Output:

json

{
  "intent_type": "live_content",
  "filters": {
    "content_tags": ["canli_yayin"],
    "names": ["TRT 1"],
    "exact_match": true
  }
}

Example 3

Input:

popüler korku dizileri

Output:

json

{
  "intent_type": "content_search",
  "filters": {
    "genres": ["korku"],
    "total_rating_min": ["P75"]
  }
}

How to Use

python

import json
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "Selenaydmrp/flan-t5-base-turkish-ott-query-parser"

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id).to(device)

PREFIX = (
    "parse_media_query: Sorgudan yalnızca bulunan medya arama filtrelerini JSON olarak çıkar. "
    "Boş alan yazma. Kullanılabilir alanlar: content_tags, genres, exclude_genres, mood_tags, "
    "names, similar_to_titles, country_names, language_names, rating_count, review_count, "
    "total_rating, release_year, exact_match. Sorgu: "
)

def parse_query(query):
    input_text = PREFIX + query

    inputs = tokenizer(
        input_text,
        return_tensors="pt",
        truncation=True,
        max_length=256
    ).to(device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=256,
            num_beams=4,
            do_sample=False
        )

    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)

    try:
        return json.loads(decoded)
    except json.JSONDecodeError:
        return {"raw_output": decoded}

query = "yerli dram dizileri olsun ama romantik olmasın"
result = parse_query(query)

print(json.dumps(result, ensure_ascii=False, indent=2))

Training Data

The model was fine-tuned on a Turkish OTT/media query parsing dataset.

The dataset contains Turkish search queries and their corresponding structured JSON outputs.

The training examples include:

genre-based queries
exclusion queries
live broadcast queries
popularity-based queries
rating-based queries
country and language filters
similar-title queries
short Turkish user queries

Evaluation

Preliminary validation results:

Metric	Value
Train Loss	0.3083
Validation Loss	0.0488
Exact Match	61.47%
Valid JSON Rate	99.78%

The Exact Match score should be interpreted carefully. In this project, Exact Match is a strict metric and can be affected by formatting differences such as extra spaces, line breaks, field ordering, or small JSON representation differences. Therefore, a prediction may be semantically correct but still counted as incorrect by Exact Match.

For this reason, the Valid JSON Rate and field-level evaluation are also important when measuring the real performance of the model.

For production use, the model should also be evaluated with:

field-level precision
field-level recall
field-level F1 score
schema validity
intent accuracy
real user queries
downstream search quality

Limitations

This model is task-specific and should not be used as a general chatbot.

Known limitations:

It may produce incorrect filters for ambiguous queries.
It may confuse mood tags and genre labels.
It may not recognize rare movie, series, actor, or channel names.
It may generate valid JSON with semantically incorrect fields.
It should be tested with real user queries before production use.

License

This model is released under the Apache 2.0 license.

Author

Developed by Selenaydmrp for Turkish OTT/media query understanding and structured search experiments.