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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-cased-finetuned-CONLL2003
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9276294098252555
- name: Recall
type: recall
value: 0.9469875462807136
- name: F1
type: f1
value: 0.9372085276482345
- name: Accuracy
type: accuracy
value: 0.9848119149938188
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-cased-finetuned-CONLL2003
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0983
- Precision: 0.9276
- Recall: 0.9470
- F1: 0.9372
- Accuracy: 0.9848
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0302 | 1.0 | 1756 | 0.0832 | 0.9055 | 0.9318 | 0.9185 | 0.9812 |
| 0.024 | 2.0 | 3512 | 0.0867 | 0.9237 | 0.9387 | 0.9311 | 0.9833 |
| 0.0123 | 3.0 | 5268 | 0.0909 | 0.9224 | 0.9438 | 0.9330 | 0.9845 |
| 0.0059 | 4.0 | 7024 | 0.0962 | 0.9218 | 0.9448 | 0.9332 | 0.9844 |
| 0.0026 | 5.0 | 8780 | 0.0983 | 0.9276 | 0.9470 | 0.9372 | 0.9848 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3