# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("master-mahdi/NER-CONLL2003")
model = AutoModelForTokenClassification.from_pretrained("master-mahdi/NER-CONLL2003")Quick Links
NER-CONLL2003
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4512
- Accuracy: 0.8841
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6127 | 1.0 | 264 | 0.5306 | 0.8567 |
| 0.5397 | 2.0 | 528 | 0.4917 | 0.8721 |
| 0.4691 | 3.0 | 792 | 0.4682 | 0.8786 |
| 0.4936 | 4.0 | 1056 | 0.4553 | 0.8829 |
| 0.5122 | 5.0 | 1320 | 0.4512 | 0.8841 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for master-mahdi/NER-CONLL2003
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="master-mahdi/NER-CONLL2003")