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BARTModel_for_Ecommerce
Rajpatel013/BARTModel_for_Ecommerce
BARTModel_for_Ecommerce is a machine learning model from Rajpatel013. 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
- May 13, 2024
- Updated
- May 13, 2024
- SHA
- 467ee27b
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. -->
BARTModel_for_Ecommerce
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: 0.6537
- Rouge1: 0.3618
- Rouge2: 0.2634
- Rougel: 0.3348
- Rougelsum: 0.336
- Gen Len: 20.0
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: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 27 | 3.3018 | 0.2994 | 0.1537 | 0.2528 | 0.2525 | 20.0 |
| No log | 2.0 | 54 | 2.2697 | 0.3287 | 0.1959 | 0.286 | 0.2866 | 20.0 |
| No log | 3.0 | 81 | 1.7739 | 0.3265 | 0.2103 | 0.2947 | 0.2954 | 20.0 |
| No log | 4.0 | 108 | 1.4085 | 0.3257 | 0.2128 | 0.2931 | 0.2937 | 20.0 |
| No log | 5.0 | 135 | 1.1230 | 0.3458 | 0.2307 | 0.3116 | 0.3124 | 20.0 |
| No log | 6.0 | 162 | 0.9408 | 0.3448 | 0.2371 | 0.3129 | 0.3139 | 20.0 |
| No log | 7.0 | 189 | 0.8269 | 0.3502 | 0.2479 | 0.3201 | 0.3212 | 20.0 |
| No log | 8.0 | 216 | 0.7584 | 0.3442 | 0.2434 | 0.3145 | 0.3157 | 20.0 |
| No log | 9.0 | 243 | 0.7075 | 0.3606 | 0.2619 | 0.3329 | 0.3339 | 20.0 |
| No log | 10.0 | 270 | 0.6890 | 0.3508 | 0.2517 | 0.3208 | 0.3219 | 20.0 |
| No log | 11.0 | 297 | 0.6761 | 0.3501 | 0.2483 | 0.3215 | 0.3228 | 20.0 |
| No log | 12.0 | 324 | 0.6631 | 0.3532 | 0.2522 | 0.3237 | 0.3245 | 20.0 |
| No log | 13.0 | 351 | 0.6573 | 0.3625 | 0.2661 | 0.3358 | 0.3372 | 20.0 |
| No log | 14.0 | 378 | 0.6528 | 0.3594 | 0.2608 | 0.3322 | 0.3333 | 20.0 |
| No log | 15.0 | 405 | 0.6537 | 0.3618 | 0.2634 | 0.3348 | 0.336 | 20.0 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Library
- transformers
- Type
- bart
- License
- apache-2.0
- Created
- May 13, 2024
- Updated
- May 13, 2024