Turkish News Summarizer (IT5-Large)
This model has been trained on a corpus of 270,000+ real, non-synthetic, and exclusively human-written Turkish news articles and their corresponding summaries. The model is optimized to adhere to the morphological structure and formal register of the Turkish news language.
Dataset Specifications
Volume: 273,046 news articles and summaries.
Type: Authentic content derived from real-time news feeds, written by professional editors.
Domains: Politics, Economy, Sports, Technology, and General Agenda.
Training Parameters and Infrastructure
The training process was executed using high-end hardware and advanced optimization techniques:
Hardware: NVIDIA B200 (Blackwell) - 180GB VRAM.
Base Model:
gsarti/it5-large(740M Parameters).Precision: BF16 Mixed Precision & TF32 Core Acceleration.
Effective Batch Size: 160 (Per device batch: 80, Gradient Accumulation: 2).
Optimizer: AdamW (Fused Kernel).
Learning Rate: 4e-5 (with Cosine Decay Scheduler).
Process Optimization: Gradient Checkpointing enabled.
Training Metrics and Loss Curve
The following graph illustrates the convergence of the Cross-Entropy Loss during the training phase:
Inference Example
from transformers import T5TokenizerFast, AutoModelForSeq2SeqLM
tokenizer = T5TokenizerFast.from_pretrained("{REPO_NAME}")
model = AutoModelForSeq2SeqLM.from_pretrained("{REPO_NAME}")
def summarize_news(text):
input_text = "summarize: " + text
inputs = tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=True)
outputs = model.generate(
inputs.input_ids,
max_length=150,
num_beams=4,
early_stopping=True
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
raw_text = "Insert news text here..."
print(summarize_news(raw_text))
License and Terms of Use
This model is released under the CC-BY-NC 4.0 license for research and development purposes. For commercial applications, the rights of the original data owners must be respected.
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