reciTAL/mlsum
Updated • 1.33k • 55
How to use Turkish-NLP/t5-efficient-small-MLSUM-TR-fine-tuned with Transformers:
# Use a pipeline as a high-level helper
# Warning: Pipeline type "summarization" 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("summarization", model="Turkish-NLP/t5-efficient-small-MLSUM-TR-fine-tuned") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Turkish-NLP/t5-efficient-small-MLSUM-TR-fine-tuned")
model = AutoModelForSeq2SeqLM.from_pretrained("Turkish-NLP/t5-efficient-small-MLSUM-TR-fine-tuned")# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Turkish-NLP/t5-efficient-small-MLSUM-TR-fine-tuned")
model = AutoModelForSeq2SeqLM.from_pretrained("Turkish-NLP/t5-efficient-small-MLSUM-TR-fine-tuned")loss=2.16, v_num=1, train_loss_step=1.980, val_loss_step=1.050, val_loss_epoch=1.820, train_loss_epoch=2.060]
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="Turkish-NLP/t5-efficient-small-MLSUM-TR-fine-tuned")