Token Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Park-Hip-02/token-classification")
model = AutoModelForTokenClassification.from_pretrained("Park-Hip-02/token-classification")Quick Links
token-classification
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2569
- Precision: 0.5474
- Recall: 0.3744
- F1: 0.4447
- Accuracy: 0.9452
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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1128 | 1.0 | 213 | 0.2483 | 0.5240 | 0.3744 | 0.4368 | 0.9445 |
| 0.0775 | 2.0 | 426 | 0.2569 | 0.5474 | 0.3744 | 0.4447 | 0.9452 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Park-Hip-02/token-classification
Base model
distilbert/distilbert-base-uncasedDataset used to train Park-Hip-02/token-classification
Evaluation results
- Precision on wnut_17test set self-reported0.547
- Recall on wnut_17test set self-reported0.374
- F1 on wnut_17test set self-reported0.445
- Accuracy on wnut_17test set self-reported0.945
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Park-Hip-02/token-classification")