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ft-distilbert-base-uncased-with-wnut17-v-0
aisuko/ft-distilbert-base-uncased-with-wnut17-v-0
ft-distilbert-base-uncased-with-wnut17-v-0 is a token classification model from aisuko. 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
- Dec 7, 2023
- Updated
- Feb 28, 2024
- SHA
- 563f7178
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Base models
Datasets
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. -->
ft-distilbert-base-uncased-with-wnut17-v-0
This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2988
- Precision: 0.4976
- Recall: 0.1909
- F1: 0.2760
- Accuracy: 0.9358
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: 32
- eval_batch_size: 32
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 107 | 0.3079 | 0.3886 | 0.0630 | 0.1085 | 0.9301 |
| No log | 2.0 | 214 | 0.2988 | 0.4976 | 0.1909 | 0.2760 | 0.9358 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.14.1
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Dec 7, 2023
- Updated
- Feb 28, 2024