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distilbert-base-uncased
mluonium/distilbert-base-uncased
distilbert-base-uncased is a token classification model from mluonium. Use it when you need labels on individual words, such as names. 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
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Mar 20, 2024
- Updated
- Mar 21, 2024
- SHA
- 69a45fcb
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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. -->
distilbert-base-uncased
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0829
- Precision: 0.7466
- Recall: 0.7574
- F1: 0.7520
- Accuracy: 0.9773
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.133 | 1.0 | 784 | 0.0769 | 0.6993 | 0.7582 | 0.7275 | 0.9762 |
| 0.0321 | 2.0 | 1568 | 0.0776 | 0.7427 | 0.7589 | 0.7507 | 0.9775 |
| 0.0213 | 3.0 | 2352 | 0.0829 | 0.7466 | 0.7574 | 0.7520 | 0.9773 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 20, 2024
- Updated
- Mar 21, 2024