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bart-large-mnli-aitools-6n
RyuExcalibur/bart-large-mnli-aitools-6n
bart-large-mnli-aitools-6n is a text classification model from RyuExcalibur. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bart
- Access
- Public
- Created
- Feb 17, 2023
- Updated
- Feb 17, 2023
- SHA
- 515f7b99
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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. -->
bart-large-mnli-aitools-6n
This model is a fine-tuned version of facebook/bart-large-mnli on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2748
- Accuracy: 0.9444
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.09 | 50 | 0.0885 | 0.9762 |
| No log | 0.18 | 100 | 0.4805 | 0.8571 |
| No log | 0.26 | 150 | 0.2582 | 0.9524 |
| No log | 0.35 | 200 | 0.2742 | 0.9286 |
| No log | 0.44 | 250 | 0.1553 | 0.9683 |
| No log | 0.53 | 300 | 0.2574 | 0.9603 |
| No log | 0.62 | 350 | 0.3690 | 0.9444 |
| No log | 0.7 | 400 | 0.3113 | 0.9365 |
| No log | 0.79 | 450 | 0.3474 | 0.9206 |
| 0.3671 | 0.88 | 500 | 0.2385 | 0.9206 |
| 0.3671 | 0.97 | 550 | 0.2947 | 0.9365 |
| 0.3671 | 1.05 | 600 | 0.2834 | 0.9444 |
| 0.3671 | 1.14 | 650 | 0.2425 | 0.9524 |
| 0.3671 | 1.23 | 700 | 0.2494 | 0.9524 |
| 0.3671 | 1.32 | 750 | 0.3040 | 0.9444 |
| 0.3671 | 1.41 | 800 | 0.2974 | 0.9444 |
| 0.3671 | 1.49 | 850 | 0.2268 | 0.9683 |
| 0.3671 | 1.58 | 900 | 0.3889 | 0.9365 |
| 0.3671 | 1.67 | 950 | 0.3333 | 0.8968 |
| 0.1777 | 1.76 | 1000 | 0.2748 | 0.9444 |
| 0.1777 | 1.85 | 1050 | 0.3463 | 0.9206 |
| 0.1777 | 1.93 | 1100 | 0.2951 | 0.9444 |
| 0.1777 | 2.02 | 1150 | 0.2726 | 0.9524 |
| 0.1777 | 2.11 | 1200 | 0.3241 | 0.9444 |
| 0.1777 | 2.2 | 1250 | 0.3543 | 0.9365 |
| 0.1777 | 2.28 | 1300 | 0.4440 | 0.9444 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- bart
- License
- mit
- Created
- Feb 17, 2023
- Updated
- Feb 17, 2023