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---
language:
- en
license: apache-2.0
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: albert-base-v2
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: default
metrics:
- name: precision
type: precision
value: 0.9252213840603477
- name: recall
type: recall
value: 0.9329732113328189
- name: f1
type: f1
value: 0.9290811285541773
- name: accuracy
type: accuracy
value: 0.9848205157332728
---
<!-- 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. -->
# albert-base-v2
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- precision: 0.9252
- recall: 0.9330
- f1: 0.9291
- 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:
- num_train_epochs: 5
- train_batch_size: 16
- learning_rate: 2e-05
- weight_decay_rate: 0.01
- num_warmup_steps: 0
- fp16: True
### Framework versions
- Transformers 4.16.2
- Pytorch 1.8.1+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
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