datasets:
- laion/gpt4v-dataset
library_name: transformers
Model Card: AdemGPT
General Information Model Name: AdemGPT Description: AdemGPT is a pre-trained generative language model that seeks to generate coherent and relevant text based on a wide spectrum of linguistic tasks.
Authors and Affiliations Authors: [Trat80] Affiliations: [N/A]
Model Functionality Supported Tasks: Text generation, Answering questions, Text to text, etc. Supported Languages: Mainly Spanish. Examples of Use: Generation of summaries, creative writing, informal conversation, among others.
Dataset and Training Dataset Origin: Created from multiple sources of text in Spanish (books, online articles, conversations, etc.). Dataset Size: Contains millions of examples of text in Spanish. Training Procedures: The GPT-3 architecture was used and trained for several weeks in a high-performance environment.
Model Performance Evaluation Metrics: Text coherence, precision in questions and answers, language fluency, etc. Results: Achieved high scores on text generation tests and language processing tasks.
Ethical Considerations Bias Considerations: Efforts have been made to mitigate bias, but there may be some inherent biases in the training data. Privacy and Security: The model does not store user information and caution should be taken when using it with sensitive data.
Limitations of the Model Known Limitations: Cannot provide information in other languages and may have difficulty with very specialized or technical concepts.
License and Conditions of Use License: [cc-by-nc-sa4.0] Conditions of Use: The model is available for non-commercial and educational use. It is recommended to review the license terms.
Ejemplo de Uso
Puedes interactuar con el modelo AdemGPT utilizando la biblioteca Transformers de Hugging Face. Aquí tienes un ejemplo simple de cómo generar texto:
from transformers import pipeline
generator = pipeline('text-generation', model='Trat80/AdemGPT')
generated_text = generator("Una vez en un lugar lejano, ", max_length=100, num_return_sequences=1)
print(generated_text)