metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2002
type: conll2002
config: es
split: validation
args: es
metrics:
- name: Precision
type: precision
value: 0.6381977967570244
- name: Recall
type: recall
value: 0.621055167429535
- name: F1
type: f1
value: 0.6295097979366338
- name: Accuracy
type: accuracy
value: 0.9309591653454259
distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the conll2002 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2431
- Precision: 0.6382
- Recall: 0.6211
- F1: 0.6295
- Accuracy: 0.9310
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 |
|---|---|---|---|---|---|---|---|
| 0.3539 | 1.0 | 521 | 0.2735 | 0.5837 | 0.5829 | 0.5833 | 0.9218 |
| 0.207 | 2.0 | 1042 | 0.2431 | 0.6382 | 0.6211 | 0.6295 | 0.9310 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0+cpu
- Datasets 3.0.1
- Tokenizers 0.20.0