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distilbert_km_10_v1_mnli
Hartunka/distilbert_km_10_v1_mnli
distilbert_km_10_v1_mnli is a text classification model from Hartunka. 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
- distilbert
- Access
- Public
- Created
- Apr 14, 2025
- Updated
- Apr 15, 2025
- SHA
- dae5569f
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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. -->
distilbert_km_10_v1_mnli
This model is a fine-tuned version of Hartunka/distilbert_km_10_v1 on the GLUE MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.7730
- Accuracy: 0.6702
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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.984 | 1.0 | 1534 | 0.9150 | 0.5618 |
| 0.8738 | 2.0 | 3068 | 0.8359 | 0.6174 |
| 0.7796 | 3.0 | 4602 | 0.7962 | 0.6512 |
| 0.6962 | 4.0 | 6136 | 0.7838 | 0.6635 |
| 0.6204 | 5.0 | 7670 | 0.8046 | 0.6658 |
| 0.5434 | 6.0 | 9204 | 0.8482 | 0.6699 |
| 0.468 | 7.0 | 10738 | 0.8990 | 0.6671 |
| 0.3994 | 8.0 | 12272 | 1.0205 | 0.6550 |
| 0.3374 | 9.0 | 13806 | 1.1317 | 0.6654 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- Languages
- en
- Created
- Apr 14, 2025
- Updated
- Apr 15, 2025