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my_awesome_wnut_model
tdineth/my_awesome_wnut_model
my_awesome_wnut_model is a token classification model from tdineth. 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
- Nov 12, 2025
- Updated
- Nov 12, 2025
- SHA
- 04da53bd
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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. -->
my_awesome_wnut_model
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: 0.0989
- Precision: 0.8596
- Recall: 0.8842
- F1: 0.8717
- Accuracy: 0.9746
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 220 | 0.0981 | 0.8553 | 0.8768 | 0.8659 | 0.9740 |
| No log | 2.0 | 440 | 0.0989 | 0.8596 | 0.8842 | 0.8717 | 0.9746 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cpu
- Datasets 4.4.1
- Tokenizers 0.22.1
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Nov 12, 2025
- Updated
- Nov 12, 2025