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deed-summarization
Hasanur525/deed-summarization
deed-summarization is a machine learning model from Hasanur525. 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
- mt5
- Access
- Public
- Created
- Jan 16, 2024
- Updated
- Jan 16, 2024
- SHA
- 36584c5a
Base models
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. -->
deed-summarization
This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 5.6954
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 55.0
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 1 | 5.6955 | 0.0 | 0.0 | 0.0 | 0.0 | 55.0 |
| No log | 2.0 | 2 | 5.6954 | 0.0 | 0.0 | 0.0 | 0.0 | 55.0 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Library
- transformers
- Type
- mt5
- Created
- Jan 16, 2024
- Updated
- Jan 16, 2024