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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9262123053131559
- name: Recall
type: recall
value: 0.9380243875153821
- name: F1
type: f1
value: 0.9320809248554913
- name: Accuracy
type: accuracy
value: 0.9839547555880344
---
<!-- 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-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0617
- Precision: 0.9262
- Recall: 0.9380
- F1: 0.9321
- Accuracy: 0.9840
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2465 | 1.0 | 878 | 0.0727 | 0.9175 | 0.9199 | 0.9187 | 0.9808 |
| 0.0527 | 2.0 | 1756 | 0.0610 | 0.9245 | 0.9361 | 0.9303 | 0.9834 |
| 0.0313 | 3.0 | 2634 | 0.0617 | 0.9262 | 0.9380 | 0.9321 | 0.9840 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.8.0
- Datasets 1.16.1
- Tokenizers 0.10.3