Summarization
Transformers
PyTorch
TensorBoard
mbart
text2text-generation
ur
seq2seq
Abstractive Summarization
Generated from Trainer
Instructions to use eslamxm/MBart-finetuned-ur-xlsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eslamxm/MBart-finetuned-ur-xlsum 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="eslamxm/MBart-finetuned-ur-xlsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("eslamxm/MBart-finetuned-ur-xlsum") model = AutoModelForSeq2SeqLM.from_pretrained("eslamxm/MBart-finetuned-ur-xlsum") - Notebooks
- Google Colab
- Kaggle
MBart-finetuned-ur-xlsum
This model is a fine-tuned version of facebook/mbart-large-50 on the xlsum dataset. It achieves the following results on the evaluation set:
- Loss: 3.2663
- Rouge-1: 40.6
- Rouge-2: 18.9
- Rouge-l: 34.39
- Gen Len: 37.88
- Bertscore: 77.06
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 5
- label_smoothing_factor: 0.1
Training results
Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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