license: cc-by-2.0
language:
- bg
pipeline_tag: text-generation
Model Card for GPT-WEB-BG
This model is pre-trained with the causal language modelling objective on a private dataset with web scraped content created at the Bulgarian Academy of Sciences under the ClaDa-BG Project.
The dataset is cleaned and balanced with a specialized procedure to avoid gender, cultural, political, racial and other biases. The procedure is described in the paper dedicated to this model- coming soon!
Model Details
Model Description
The model is the first from a series of Large Languege Models for Bulgarian.
- Developed by: Iva Marinova
- Shared by [optional]: ClaDa-BG, : National Interdisciplinary Research E-Infrastructure for Bulgarian Language and Cultural Heritage Resources and Technologies integrated within European CLARIN and DARIAH infrastructures
- Model type: GPT-2
- Language(s) (NLP): Bulgarian
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: Marinova et. al. 2023 - link to be added
- Demo [optional]: [More Information Needed]
Uses
The model is trained on the causal language modeling objective and can be used to generate content based on textual input. It can be further finetuned for specific NLP tasks in the online media domain such as Event Extraction, Relation Extracation, Named Entity Recognition, etc. This model is intended for use from researchers and practitioners in the NLP field.
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
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.
Using pipeline
from transformers import pipeline, set_seed
gpt_web_bg = pipeline('text-generation', model='/usmiva/gpt_web_bg', max_length=50, num_beams=3, temperature=0.8)
set_seed(42)
gpt_web_bg("По професия той е ")
[{'generated_text': 'По професия той е строителен работник, който е �'}]
Training Details
Training Data
Training Procedure
Preprocessing [optional]
The process of creating a diverse, bias-proof, and ethically fair dataset requires a meticulous and effective approach to clean the raw text data extracted from the internet. To address this challenge, we propose a specialized, multi-step procedure organized into the following stages:
Deduplication - Duplicate text sequences, often caused by web scraping, are removed from the dataset, thus ensuring that each entry contributes unique information to the training data. Topic Classification - To guarantee diverse subject matter and reduce the risk of topic bias, topic classification is employed to categorize text entries based on their content. Sentiment Classification - By categorizing entries with sentiment, the dataset diversity is further enhanced, enabling models to better interpret and handle the inherent emotional aspects of human language. Hate-Speech Detection - To exclude content promoting hate speech from the dataset, automatic detection methods for Bulgarian are utilized. Balancing Topics and Sentiment in the Data - The emphasis is placed on ensuring an adequate balance between topics and sentiment classes, as an imbalanced dataset can lead to biased results. By carefully redistributing instances across topics and sentiment categories, a more representative and inclusive dataset can be assembled, resulting in more robust and adaptable models. Cleaning Abusive Content - To further refine the dataset, abusive content, including profanities, vulgar language, and other offensive expressions were cleaned from the text utilizing algorithms for abusive language detection. Minimum Sentence Threshold - To ensure that the dataset includes meaningful and coherent text instances, a minimum sentence threshold is imposed, requiring that each entry contains at least five sentences. This condition ensures that models are trained on richer linguistic contexts and promotes more accurate and nuanced text generation. Cleaning non Bulgarian content
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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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
The model is trained BERT with the masked language modelling objective from scratch on Bulgarian dataset containing data from opensource online media.
Compute Infrastructure
1 NVIDIA V100 video card
Hardware
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Software
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Citation [optional]
Transformer-Based Language Models for Bulgarian, Marinova et. al. - TBA
BibTeX:
TBA
APA:
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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