Instructions to use nmsofficial/NedoTranslator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nmsofficial/NedoTranslator with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="nmsofficial/NedoTranslator")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("nmsofficial/NedoTranslator") model = AutoModelForSeq2SeqLM.from_pretrained("nmsofficial/NedoTranslator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
NedoTranslator
NedoTranslator is an English-to-Turkish machine translation model fine-tuned from Helsinki-NLP/opus-mt-tc-big-en-tr at base revision e539fc16a8a1a0ea5950eb339b595bfcce990e90.
Model details
- Task: English to Turkish machine translation
- Architecture: MarianMT
- Parameters: 236,883,968
- Checkpoint dtype: float32
- Recommended decoding for benchmark parity: beam search with num_beams=4
- License: CC BY 4.0, following the base model license
Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model_id = "nmsofficial/NedoTranslator"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
text = "Artificial intelligence is changing the way software is built."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
outputs = model.generate(**inputs, num_beams=4, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Provenance
This Hub release corresponds to the local NedoTranslator production v1 checkpoint used in the September 2026 EN-to-TR benchmark runs. The uploaded bundle contains only the standard Transformers inference assets and excludes cluster-specific paths, training manifests, and local benchmark artifacts.
Model weights SHA-256: 0344f97adf437f4aaa5cd63da05d4fa5a59bbab483c25165d37fecf7e799a5f5
Attribution
Base model: Helsinki-NLP, opus-mt-tc-big-en-tr. Please retain attribution and follow the CC BY 4.0 license terms when redistributing or adapting this model.
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Base model
Helsinki-NLP/opus-mt-tc-big-en-tr