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mbert2mbert-arabic-text-summarization-finetuned-xsum_arabic_abstractive_final_finaln
lilouuch/mbert2mbert-arabic-text-summarization-finetuned-xsum_arabic_abstractive_final_finaln
mbert2mbert-arabic-text-summarization-finetuned-xsum_arabic_abstractive_final_finaln is a machine learning model from lilouuch. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- encoder-decoder
- Access
- Public
- Created
- Nov 4, 2022
- Updated
- Nov 4, 2022
- SHA
- 6cb343ac
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
mbert2mbert-arabic-text-summarization-finetuned-xsum_arabic_abstractive_final_finaln
This model is a fine-tuned version of malmarjeh/mbert2mbert-arabic-text-summarization on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.2826
- Rouge1: 0.0119
- Rouge2: 0.0
- Rougel: 0.0119
- Rougelsum: 0.0119
- Gen Len: 41.8856
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.5104 | 1.0 | 7915 | 2.3684 | 0.0 | 0.0 | 0.0 | 0.0 | 41.8314 |
| 2.2222 | 2.0 | 15830 | 2.2826 | 0.0119 | 0.0 | 0.0119 | 0.0119 | 41.8856 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
- Library
- transformers
- Type
- encoder-decoder
- Created
- Nov 4, 2022
- Updated
- Nov 4, 2022