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keyphrase-extractions_roberta_large
tmsreekanth98/keyphrase-extractions_roberta_large
keyphrase-extractions_roberta_large is a machine learning model from tmsreekanth98. 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
- Dec 29, 2022
- Updated
- Feb 16, 2023
- SHA
- faab255c
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. -->
ED_keyphrase_roberta/
This model is a fine-tuned version of on the kp20k dataset. It achieves the following results on the evaluation set:
- Loss: 4.6070
- Rouge1: 0.1132
- Rouge2: 0.0161
- Rougel: 0.108
- Rougelsum: 0.1081
- Gen Len: 10.9056
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 6.2014 | 1.0 | 664 | 5.4438 | 0.0532 | 0.0021 | 0.0525 | 0.0524 | 9.0955 |
| 5.3993 | 2.0 | 1328 | 4.8016 | 0.0958 | 0.0105 | 0.0921 | 0.0921 | 11.524 |
| 4.9398 | 3.0 | 1992 | 4.6499 | 0.1095 | 0.0153 | 0.1049 | 0.1048 | 11.2748 |
| 4.6497 | 4.0 | 2656 | 4.6070 | 0.1132 | 0.0161 | 0.108 | 0.1081 | 10.9056 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
- Library
- transformers
- Type
- encoder-decoder
- Created
- Dec 29, 2022
- Updated
- Feb 16, 2023