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 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.
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
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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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).
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- Hours used: [More Information Needed]
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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Glossary [optional]
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