Instructions to use NiuTrans/LMT-60-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NiuTrans/LMT-60-8B 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="NiuTrans/LMT-60-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NiuTrans/LMT-60-8B") model = AutoModelForCausalLM.from_pretrained("NiuTrans/LMT-60-8B") - Notebooks
- Google Colab
- Kaggle
The Turkish-English translation model that achieved the highest Bleu score with the Flores test dataset.
#3
by NovaYear - opened
BLEU Skore: 41.45
Detailed Scores: [70.87941236482351, 47.636874122601775, 34.42178684081923, 25.410255218260424]
Brevity Penalty (BP): 1.000
Reference Length: 24360
Hypothesis Length: 24505
However, despite the excellent results, the translation process is very slow. Translating 1000 Flores test data points took almost 30 minutes. Alternatively, using QuickMT models, which translate 1000 data points in just 2 minutes, might be much more time-efficient. I should also mention that the QuickMT model has a SacreBleu score of 39.44.