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---
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
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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](https://huggingface.co/Akirami)
- **Model type:** DistillBert
- **License:** MIT
- **Finetuned from model [optional]:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [Akirami/distillbert-uncased-ag-news](https://huggingface.co/Akirami/distillbert-uncased-ag-news)
## How to Get Started with the Model
Use the code below to get started with the model.
```python
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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[AG News Dataset](https://huggingface.co/datasets/fancyzhx/ag_news)
### Training Procedure
The model has been trained through Knowledge Distillation, where the teacher model is [nateraw/bert-base-uncased-ag-news](https://huggingface.co/nateraw/bert-base-uncased-ag-news) and the student model is [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased)
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** Trained in fp16 format
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### 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
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [T4 GPU]
- **Hours used:** [25 Minutes]
- **Cloud Provider:** [Google Colab]
- **Carbon Emitted:** [0.01] |