Token Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("bhadauriaupendra062/tokenclassificationmodel")
model = AutoModelForTokenClassification.from_pretrained("bhadauriaupendra062/tokenclassificationmodel")Quick Links
tokenclassificationmodel
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: 0.2719
- Precision: 0.5423
- Recall: 0.2910
- F1: 0.3788
- Accuracy: 0.9408
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2835 | 0.4465 | 0.2048 | 0.2808 | 0.9363 |
| No log | 2.0 | 426 | 0.2719 | 0.5423 | 0.2910 | 0.3788 | 0.9408 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for bhadauriaupendra062/tokenclassificationmodel
Base model
distilbert/distilbert-base-uncasedDataset used to train bhadauriaupendra062/tokenclassificationmodel
Evaluation results
- Precision on wnut_17test set self-reported0.542
- Recall on wnut_17test set self-reported0.291
- F1 on wnut_17test set self-reported0.379
- Accuracy on wnut_17test set self-reported0.941
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bhadauriaupendra062/tokenclassificationmodel")