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distillbert-uncased-ag-news
Akirami/distillbert-uncased-ag-news
distillbert-uncased-ag-news is a text classification model from Akirami. Use it when you need a label for a piece of text. It is set up for transformers.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- distilbert
- Access
- Public
- Created
- Aug 23, 2024
- Updated
- Aug 23, 2024
- SHA
- da98b897
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Datasets
Model card
From the Hugging Face model README
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
- Model type: DistillBert
- License: MIT
- Finetuned from model [optional]: distilbert/distilbert-base-uncased
Model Sources
<!-- Provide the basic links for the model. -->- 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
<!-- 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. -->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]
<!-- 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 presented in Lacoste et al. (2019).
- Hardware Type: [T4 GPU]
- Hours used: [25 Minutes]
- Cloud Provider: [Google Colab]
- Carbon Emitted: [0.01]
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- Created
- Aug 23, 2024
- Updated
- Aug 23, 2024