metadata
library_name: transformers
datasets:
- fancyzhx/ag_news
metrics:
- accuracy
model-index:
- name: distillbert-uncased-ag-news
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
Akirami/distillbert-uncased-ag-news
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Akirami
- Model type: DistillBert
- License: MIT
- Finetuned from model [optional]: distilbert/distilbert-base-uncased
Model Sources
- Repository: Akirami/distillbert-uncased-ag-news
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Akirami/distillbert-uncased-ag-news")
model = AutoModelForSequenceClassification.from_pretrained("Akirami/distillbert-uncased-ag-news")
Training Details
Training Data
Training Procedure
The model has been trained through Knowledge Distillation, where the teacher model is nateraw/bert-base-uncased-ag-news and the student model is distilbert/distilbert-base-uncased
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: Trained in fp16 format
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
The test portion of AG News data is used for testing
Metrics
Classification Report:
| Class | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| 0 | 0.95 | 0.92 | 0.94 | 1900 |
| 1 | 0.98 | 0.98 | 0.98 | 1900 |
| 2 | 0.90 | 0.88 | 0.89 | 1900 |
| 3 | 0.88 | 0.92 | 0.90 | 1900 |
| Accuracy | 0.93 | 7600 | ||
| Macro Avg | 0.93 | 0.93 | 0.93 | 7600 |
| Weighted Avg | 0.93 | 0.93 | 0.93 | 7600 |
Balanced Accuracy Score: 0.926578947368421
Accuracy Score: 0.9265789473684211
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [T4 GPU]
- Hours used: [25 Minutes]
- Cloud Provider: [Google Colab]
- Carbon Emitted: [0.01]