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distil-bert-fintuned-issues-cfpb-complaints
Mahesh9/distil-bert-fintuned-issues-cfpb-complaints
distil-bert-fintuned-issues-cfpb-complaints is a text classification model from Mahesh9. Use it when you need a label for a piece of text. 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
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Mar 31, 2024
- Updated
- Apr 12, 2024
- SHA
- ca3ab983
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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. -->
distil-bert-fintuned-issues-cfpb-complaints
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6711
- Accuracy: 0.7652
- Precision: 0.5935
- Recall: 0.5577
- F1: 0.5702
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.7269 | 1.0 | 11603 | 0.7187 | 0.7438 | 0.5853 | 0.5327 | 0.5491 |
| 0.612 | 2.0 | 23206 | 0.6711 | 0.7652 | 0.5935 | 0.5577 | 0.5702 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.1
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 31, 2024
- Updated
- Apr 12, 2024