Unified Model Records

BLOOMZ

Type: family

Publisher: BigScience Workshop Released: 2022-11-17 v.1.0.0

Model details

Blackbox External Model Access
Capabilities demonstration
Capabilities description
Centralized model documentation
Evaluation of capabilities
External model access protocol
External reproducibility of capabilities evaluation
External reproducibility of intentional harm evaluation -
External reproducibility of mitigations evaluation -
External reproducibility of trustworthiness evaluation -
External reproducibility of unintentional harm evaluation -
Full external model access
Inference compute evaluation -
Inference duration evaluation
Input modality
Intentional harm evaluation -
Limitations demonstration -
Limitations description
Mitigations demonstration -
Mitigations description -
Mitigations evaluation -
Model architecture
Asset license
Model components
Model size
Output modality
Risks demonstration -
Risks description -
Third party capabilities evaluation -
Third party evaluation of limitations
Third party mitigations evaluation -
Third party risks evaluation -
Trustworthiness evaluation -
Unintentional harm evaluation -

Intended use

  • Natural language processing tasks, including but not limited to translation, sentiment analysis, and question answering.
  • Cross-lingual understanding and generation tasks.
  • Instruction-based prompt generation for a wide range of languages.
  • Zero-shot and few-shot learning applications.
  • Exploratory data analysis and research in multilingual language model capabilities.

Dependencies

Metrics

No metrics specified.

Environmental

No environmental refs specified.

Carbon emitted (tCO2eq): 0

Energy usage: 0 w

Compute usage: 0

Ethical considerations

  • Potential for biased or inaccurate outputs across less-supported languages, requiring careful validation.
  • Use of the model in applications with impactful consequences should be approached with caution.
  • Need for transparency regarding the training data sources and model limitations to users.
  • Ethical considerations around data privacy and consent, especially in multilingual contexts.
  • Awareness of cultural sensitivity and potential for reinforcing stereotypes must be considered in model application and development.

Recommendations

  • Employment of early stopping, addition of long tasks, and minimum generation length forcing for improved generative task performance.
  • Fine-tuning with both English and machine-translated multilingual prompts for enhanced cross-lingual abilities.
  • Utilization of the model in research to explore and expand the boundaries of zero-shot learning across languages.
  • Adoption of ethical and fair use practices, considering the model's broad linguistic capabilities.
  • Engagement with the BigScience community for collaborative research and development efforts.