Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
bloom
Eval Results
text-generation-inference
Inference Endpoints
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  <details>
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  <summary>Click to expand</summary>
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  See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
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  | Task | Language | Metric | BLOOM-176B | OPT-175B* |
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  | humaneval | python | pass@10 | 0.322 | 0.0 |
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  | humaneval | python | pass@100 | 0.555 | 0.003 |
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- ## Metrics
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- *This section describes the different ways performance is calculated and why.*
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-
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-
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- Includes:
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- | Metric | Why chosen |
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- |--------------------|--------------------------------------------------------------------|
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- | [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
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- | Cross Entropy [Loss](#loss) | Standard objective for language models. |
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- And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
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-
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- ## Factors
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- *This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
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-
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- - Language, such as English or Yoruba
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-
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- - Domain, such as newswire or stories
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- - Demographic characteristics, such as gender or nationality
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-
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- ## Results
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- *Results are based on the [Factors](#factors) and [Metrics](#metrics).*
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  **Train-time Evaluation:**
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  - Perplexity: 8.9
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- (More evaluation scores forthcoming.)
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  </details>
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  <details>
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  <summary>Click to expand</summary>
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+ ## Metrics
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+ *This section describes the different ways performance is calculated and why.*
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+
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+
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+ Includes:
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+
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+ | Metric | Why chosen |
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+ |--------------------|--------------------------------------------------------------------|
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+ | [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
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+ | Cross Entropy [Loss](#loss) | Standard objective for language models. |
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+
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+ And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
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+
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+ ## Factors
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+ *This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
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+
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+ - Language, such as English or Yoruba
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+
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+ - Domain, such as newswire or stories
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+
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+ - Demographic characteristics, such as gender or nationality
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+
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+ ## Results
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+ *Results are based on the [Factors](#factors) and [Metrics](#metrics).*
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+
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  See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
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  | Task | Language | Metric | BLOOM-176B | OPT-175B* |
 
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  | humaneval | python | pass@10 | 0.322 | 0.0 |
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  | humaneval | python | pass@100 | 0.555 | 0.003 |
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  **Train-time Evaluation:**
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  - Perplexity: 8.9
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  </details>
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