Hugging Face's logo

Mawiwawi
/
distilbert-base-uncased-finetuned-ner

Token Classification
Transformers
Safetensors
distilbert
Generated from Trainer
Inference Endpoints
Model card Files Community
distilbert-base-uncased-finetuned-ner / README.md
Mawiwawi's picture
Mawiwawi
End of training
a3a3678 verified
preview code
|
raw
history blame contribute delete
No virus
3.41 kB
metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilbert-base-uncased-finetuned-ner
    results: []

distilbert-base-uncased-finetuned-ner

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2988
  • Precision: 0.8066
  • Recall: 0.7644
  • F1: 0.7849
  • Accuracy: 0.9432

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: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 13 1.7619 0.0 0.0 0.0 0.6185
No log 2.0 26 1.4482 0.0526 0.0052 0.0095 0.6985
No log 3.0 39 1.0747 0.0417 0.0105 0.0167 0.7429
No log 4.0 52 0.8462 0.2262 0.0995 0.1382 0.7821
No log 5.0 65 0.6852 0.3290 0.2670 0.2948 0.8172
No log 6.0 78 0.5970 0.4346 0.4869 0.4593 0.8684
No log 7.0 91 0.5108 0.5072 0.5497 0.5276 0.8880
No log 8.0 104 0.4515 0.5882 0.6283 0.6076 0.9086
No log 9.0 117 0.4105 0.6305 0.6702 0.6497 0.9169
No log 10.0 130 0.3755 0.7120 0.6859 0.6987 0.9293
No log 11.0 143 0.3661 0.7243 0.7016 0.7128 0.9293
No log 12.0 156 0.3460 0.7273 0.7120 0.7196 0.9313
No log 13.0 169 0.3287 0.7609 0.7330 0.7467 0.9355
No log 14.0 182 0.3177 0.7701 0.7539 0.7619 0.9370
No log 15.0 195 0.3133 0.7705 0.7382 0.7540 0.9360
No log 16.0 208 0.3028 0.7826 0.7539 0.7680 0.9406
No log 17.0 221 0.3062 0.7944 0.7487 0.7709 0.9391
No log 18.0 234 0.3015 0.8011 0.7592 0.7796 0.9411
No log 19.0 247 0.2997 0.7935 0.7644 0.7787 0.9432
No log 20.0 260 0.2988 0.8066 0.7644 0.7849 0.9432

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.3.0+cpu
  • Datasets 2.19.1
  • Tokenizers 0.19.1