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TXT-to-TXT
WasibMehmood/TXT-to-TXT
TXT-to-TXT is a machine learning model from WasibMehmood. 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. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- bart
- Access
- Public
- Created
- Sep 24, 2024
- Updated
- Sep 27, 2024
- SHA
- dfc14aaf
Base models
Datasets
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. -->
cleaned_ds
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.3404
- Rouge1: 0.2705
- Rouge2: 0.0363
- Rougel: 0.1609
- Rougelsum: 0.1609
- Generated Length: 113.0
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
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 1 | 5.0242 | 0.2692 | 0.0362 | 0.1676 | 0.1676 | 83.5 |
| No log | 2.0 | 2 | 4.5239 | 0.2629 | 0.0251 | 0.1431 | 0.1431 | 96.5 |
| No log | 3.0 | 3 | 4.3404 | 0.2705 | 0.0363 | 0.1609 | 0.1609 | 113.0 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
- Library
- transformers
- Type
- bart
- License
- apache-2.0
- Languages
- en
- Created
- Sep 24, 2024
- Updated
- Sep 27, 2024