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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
- f1
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-finetuned-news
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: ag_news
type: ag_news
args: default
metrics:
- type: accuracy
value: 0.9388157894736842
name: Accuracy
- type: f1
value: 0.9388275184627893
name: F1
---
<!-- 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-news
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2117
- Accuracy: 0.9388
- F1: 0.9388
## Model description
This model is intended to categorize news headlines into one of four categories; World, Sports, Science & Technology, or Business
## Intended uses & limitations
The model is limited by the training data it used. If you use the model with a news story that falls outside of the four intended categories, it produces quite confused results.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2949 | 1.0 | 3750 | 0.2501 | 0.9262 | 0.9261 |
| 0.1569 | 2.0 | 7500 | 0.2117 | 0.9388 | 0.9388 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|