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all_2490_bart-base
baek26/all_2490_bart-base
all_2490_bart-base is a machine learning model from baek26. 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
- Apr 22, 2024
- Updated
- Apr 23, 2024
- SHA
- c0930b11
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. -->
all_2490_bart-base
This model is a fine-tuned version of facebook/bart-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0206
- Rouge1: 0.2426
- Rouge2: 0.1208
- Rougel: 0.2025
- Rougelsum: 0.2266
- Gen Len: 19.9945
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: 32
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.7151 | 0.8 | 500 | 1.1257 | 0.2361 | 0.1122 | 0.1955 | 0.2196 | 19.9978 |
| 1.0837 | 1.61 | 1000 | 1.0810 | 0.2401 | 0.1176 | 0.1997 | 0.2237 | 19.9953 |
| 1.0348 | 2.41 | 1500 | 1.0651 | 0.2401 | 0.1179 | 0.1999 | 0.2239 | 19.9957 |
| 1.0059 | 3.21 | 2000 | 1.0522 | 0.2402 | 0.1183 | 0.2001 | 0.2242 | 19.996 |
| 0.9855 | 4.02 | 2500 | 1.0439 | 0.2416 | 0.1197 | 0.2014 | 0.2257 | 19.9948 |
| 0.9642 | 4.82 | 3000 | 1.0361 | 0.2421 | 0.12 | 0.2019 | 0.2263 | 19.9936 |
| 0.9519 | 5.63 | 3500 | 1.0329 | 0.2415 | 0.1199 | 0.2016 | 0.2258 | 19.9948 |
| 0.9389 | 6.43 | 4000 | 1.0278 | 0.2424 | 0.1204 | 0.2022 | 0.2265 | 19.9942 |
| 0.9302 | 7.23 | 4500 | 1.0273 | 0.2422 | 0.1204 | 0.2022 | 0.2264 | 19.9943 |
| 0.9225 | 8.04 | 5000 | 1.0219 | 0.2421 | 0.1208 | 0.2023 | 0.2263 | 19.9946 |
| 0.9152 | 8.84 | 5500 | 1.0219 | 0.2429 | 0.1208 | 0.2027 | 0.227 | 19.9948 |
| 0.911 | 9.64 | 6000 | 1.0206 | 0.2426 | 0.1208 | 0.2025 | 0.2266 | 19.9945 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
- Library
- transformers
- Type
- bart
- License
- apache-2.0
- Created
- Apr 22, 2024
- Updated
- Apr 23, 2024