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Add evaluation results on ag_news dataset
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---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- ag_news
- pytorch
license: mit
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: nateraw/bert-base-uncased-ag-news
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.9414473684210526
verified: true
- name: Precision Macro
type: precision
value: 0.9416084922682436
verified: true
- name: Precision Micro
type: precision
value: 0.9414473684210526
verified: true
- name: Precision Weighted
type: precision
value: 0.9416084922682435
verified: true
- name: Recall Macro
type: recall
value: 0.9414473684210527
verified: true
- name: Recall Micro
type: recall
value: 0.9414473684210526
verified: true
- name: Recall Weighted
type: recall
value: 0.9414473684210526
verified: true
- name: F1 Macro
type: f1
value: 0.9414706154045142
verified: true
- name: F1 Micro
type: f1
value: 0.9414473684210526
verified: true
- name: F1 Weighted
type: f1
value: 0.9414706154045143
verified: true
- name: loss
type: loss
value: 0.17173650860786438
verified: true
---
# bert-base-uncased-ag-news
## Model description
`bert-base-uncased` finetuned on the AG News dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 4 T4 GPUs, 4 epochs. [The code can be found here](https://github.com/nateraw/hf-text-classification)
#### Limitations and bias
- Not the best model...
## Training data
Data came from HuggingFace's `datasets` package. The data can be viewed [on nlp viewer](https://huggingface.co/nlp/viewer/?dataset=ag_news).
## Training procedure
...
## Eval results
...