metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- conllpp
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: conllpp_NER
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conllpp
type: conllpp
config: conllpp
split: test
args: conllpp
metrics:
- name: Precision
type: precision
value: 0.8583545377438507
- name: Recall
type: recall
value: 0.8874079270431428
- name: F1
type: f1
value: 0.8726394757264812
- name: Accuracy
type: accuracy
value: 0.9747427663835371
conllpp_NER
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the conllpp dataset. It achieves the following results on the evaluation set:
- Loss: 0.0877
- Precision: 0.8584
- Recall: 0.8874
- F1: 0.8726
- Accuracy: 0.9747
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 439 | 0.0943 | 0.8399 | 0.8700 | 0.8547 | 0.9716 |
| 0.2003 | 2.0 | 878 | 0.0877 | 0.8584 | 0.8874 | 0.8726 | 0.9747 |
Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0