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model_3_epochs_no_perturb
cria111/model_3_epochs_no_perturb
model_3_epochs_no_perturb is a token classification model from cria111. 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
- May 1, 2024
- Updated
- May 1, 2024
- SHA
- bc03ccfb
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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. -->
model_3_epochs_no_perturb
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: 0.1620
- Precision: 0.2876
- Recall: 0.3063
- F1: 0.2967
- Accuracy: 0.9558
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 103 | 0.1906 | 0.2105 | 0.1778 | 0.1928 | 0.9508 |
| No log | 2.0 | 206 | 0.1676 | 0.2550 | 0.3016 | 0.2764 | 0.9534 |
| No log | 3.0 | 309 | 0.1620 | 0.2876 | 0.3063 | 0.2967 | 0.9558 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.0+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- May 1, 2024
- Updated
- May 1, 2024