- name
- bloomz-7b1-mt
- version
- 1.0.0
- publisher
- BigScience Workshop
- release date
- 2022-11-17
- model type
- Multitask Fine-tuned Language Model
bloomz-7b1-mt
Type: model
Publisher: BigScience Workshop
Released: 2022-11-17
v.1.0.0
Metadata
General information.
Relations
Relationship Graph
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.
Factors
- Language support and proficiency across a broad spectrum of languages.
- The clarity and specificity of instruction prompts.
- Model scalability and performance across different sizes from 300M to 176B parameters.
- Generalization abilities to unseen tasks and languages.
- Accessibility and ease of use for researchers and developers with different levels of resources.
Evaluation Data
- Description: A diverse set of evaluation tasks covering coreference resolution, natural language inference, sentence completion, and program synthesis across multiple languages.
- Description: Datasets from the Winogrande, ANLI, XNLI, and HumanEval evaluations, allowing for an extensive assessment of model performance in both seen and unseen languages.
- Description: Validation and test splits are utilized from the respective datasets to ensure unbiased evaluation.
- Description: Multilingual task evaluation employing prompts in both English and the respective native languages to gauge cross-lingual transfer capabilities.
- Description: Benchmarking against existing models like XGLM, T0, and GPT to understand the competitive landscape.
Training Data
- Description: The model utilizes the BIG-bench xP3 dataset for training, promoting a wide coverage of tasks and languages.
- Description: Incorporation of code and programming languages alongside natural languages to enhance the model's versatility.
- Description: Utilized datasets such as BIG-bench, ROOTS, and a subset of the mC4 corpus to provide rich, diverse linguistic and task coverage.
- Description: Finetuning approach on xP3, xP3mt, and P3 datasets to enable cross-lingual generalization and effective prompt-based task performance.
- Description: Leverages both pretrained (BLOOM, mT5) and bespoke large language models across various sizes for targeted task learning.
Additional Information
- The project is conducted under the BigScience initiative, allowing for open collaboration and research.
- Models are released under RAIL and Apache 2.0 licenses for wide accessibility and use.
- Fine-tuned models incorporate biases towards short answers, affecting performance on generative tasks.
- Language contamination analysis in the pretraining corpus shows unintentional learning from 'unseen' languages.
- Recommendations include using a specific prompting format and considering model size according to task requirements.
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.