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rlt_2409_1450
RebekkaB/rlt_2409_1450
rlt_2409_1450 is a text classification model from RebekkaB. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Sep 24, 2022
- Updated
- Sep 24, 2022
- SHA
- 36fbe891
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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. -->
rlt_2409_1450
This model is a fine-tuned version of svalabs/gbert-large-zeroshot-nli on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0518
- F1: 0.9826
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 0.99 | 36 | 0.5165 | 0.8542 |
| No log | 1.99 | 72 | 0.1459 | 0.9599 |
| No log | 2.99 | 108 | 0.0733 | 0.9882 |
| No log | 3.99 | 144 | 0.1385 | 0.9502 |
| No log | 4.99 | 180 | 0.0948 | 0.9806 |
| No log | 5.99 | 216 | 0.0699 | 0.9822 |
| No log | 6.99 | 252 | 0.0582 | 0.9859 |
| No log | 7.99 | 288 | 0.0340 | 0.9933 |
| No log | 8.99 | 324 | 0.0475 | 0.9826 |
| No log | 9.99 | 360 | 0.0518 | 0.9826 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Sep 24, 2022
- Updated
- Sep 24, 2022