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---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: lora-ner
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: 0.8535113174695299
- name: Recall
type: recall
value: 0.8726560645620698
- name: F1
type: f1
value: 0.8629775247931459
- name: Accuracy
type: accuracy
value: 0.9730680240629338
---
<!-- 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. -->
# lora-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0920
- Precision: 0.8535
- Recall: 0.8727
- F1: 0.8630
- Accuracy: 0.9731
## 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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 220 | 0.1085 | 0.8026 | 0.8314 | 0.8167 | 0.9675 |
| No log | 2.0 | 440 | 0.0804 | 0.8693 | 0.8818 | 0.8755 | 0.9759 |
| 0.2014 | 3.0 | 660 | 0.0720 | 0.8764 | 0.8970 | 0.8866 | 0.9783 |
| 0.2014 | 4.0 | 880 | 0.0688 | 0.8773 | 0.9056 | 0.8912 | 0.9792 |
| 0.0882 | 5.0 | 1100 | 0.0674 | 0.8823 | 0.9067 | 0.8943 | 0.9796 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3
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