Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 61
Model Card for Model ID
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: [More Information Needed]
- Model type: GPT-2 for Sequence Classification
- Language(s) (NLP): English (en)
- License: [More Information Needed]
- Finetuned from model [optional]: gpt2 by HuggingFace
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
This fine-tuned GPT-2 model is intended for sentiment classification of tweets, categorizing them into three sentiment labels.
Downstream Use [optional]
The model can be fine-tuned further for other text classification tasks.
Out-of-Scope Use
Not suitable for use cases that require long-context comprehension or real-time sentiment analysis on large datasets
Bias, Risks, and Limitations
Bias: Since the training data is from a specific dataset (mteb/tweet_sentiment_extraction), the model might exhibit biases inherent to social media data.
Limitations: May not generalize well to non-social media text or languages other than English.
Recommendations
Be cautious of potential biases when interpreting predictions, especially for sensitive content.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import GPT2Tokenizer, GPT2ForSequenceClassification
tokenizer = GPT2Tokenizer.from_pretrained("gpt2") model = GPT2ForSequenceClassification.from_pretrained("your-finetuned-model-id")
inputs = tokenizer("Your text here", return_tensors="pt") outputs = model(**inputs) logits = outputs.logits
Training Details
Training Data
Dataset: mteb/tweet_sentiment_extraction (Tweets labeled with sentiment)
Training Procedure
Batch Size: 1 (with gradient accumulation steps of 4) Model: GPT-2 Optimizer: Default settings in HuggingFace Trainer
Preprocessing [optional]
[More Information Needed]
Testing Data and Metrics Testing Dataset: Subset of mteb/tweet_sentiment_extraction Metrics: Accuracy (using the evaluate library)
Training Hyperparameters
Learning Rate: Default for HuggingFace Trainer Epochs:
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
- Downloads last month
- -