Data as of October 1, 2026 · How the score is built
LPS (FinancialPhraseBank)
Decision readingLPS (FinancialPhraseBank) is tracked, but not publicly ranked yet. The original benchmark tables below include the published metrics for this evaluated configuration. Their distinct protocols remain outside general model rankings.
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Original benchmark results
Every published metric for this configuration, with source precision and evaluation limits. Download results (JSON) · Numeric metrics (CSV)
Class metrics at 100% and >75% agreement
The original study reports ten-fold cross-validation at four annotator-agreement thresholds. Its class-specific accuracy, precision, recall, and F1 are ratios from 0 to 1. These are not overall sentiment accuracy or Perplexity’s sampled panel. No unified modern leaderboard is published in the reviewed benchmark-owner sources.
All metrics are 0–1 ratios. SVM-MPQA is the paper’s baseline marked with footnote a.
Published source · Full FinancialPhraseBank results
Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts — Pekka Malo and colleagues. Numeric results transcribed and reformatted; evaluation notes and model links added by BenchLM. Published numeric precision is retained.
| Agreement | Class | Metric | LPS |
|---|---|---|---|
| 100% | Positive | Accuracy | 0.869 |
| 100% | Positive | Recall | 0.737 |
| 100% | Positive | Precision | 0.742 |
| 100% | Positive | F1-score | 0.739 |
| 100% | Neutral | Accuracy | 0.828 |
| 100% | Neutral | Recall | 0.868 |
| 100% | Neutral | Precision | 0.854 |
| 100% | Neutral | F1-score | 0.861 |
| 100% | Negative | Accuracy | 0.951 |
| 100% | Negative | Recall | 0.789 |
| 100% | Negative | Precision | 0.839 |
| 100% | Negative | F1-score | 0.813 |
| >75% | Positive | Accuracy | 0.836 |
| >75% | Positive | Recall | 0.658 |
| >75% | Positive | Precision | 0.690 |
| >75% | Positive | F1-score | 0.674 |
| >75% | Neutral | Accuracy | 0.792 |
| >75% | Neutral | Recall | 0.837 |
| >75% | Neutral | Precision | 0.830 |
| >75% | Neutral | F1-score | 0.833 |
| >75% | Negative | Accuracy | 0.945 |
| >75% | Negative | Recall | 0.800 |
| >75% | Negative | Precision | 0.760 |
| >75% | Negative | F1-score | 0.780 |
Class metrics at >66% and >50% agreement
The original study reports ten-fold cross-validation at four annotator-agreement thresholds. Its class-specific accuracy, precision, recall, and F1 are ratios from 0 to 1. These are not overall sentiment accuracy or Perplexity’s sampled panel. No unified modern leaderboard is published in the reviewed benchmark-owner sources.
All metrics are 0–1 ratios. SVM-MPQA is the paper’s baseline marked with footnote a.
Published source · Full FinancialPhraseBank results
Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts — Pekka Malo and colleagues. Numeric results transcribed and reformatted; evaluation notes and model links added by BenchLM. Published numeric precision is retained.
| Agreement | Class | Metric | LPS |
|---|---|---|---|
| >66% | Positive | Accuracy | 0.798 |
| >66% | Positive | Recall | 0.816 |
| >66% | Positive | Precision | 0.599 |
| >66% | Positive | F1-score | 0.691 |
| >66% | Neutral | Accuracy | 0.753 |
| >66% | Neutral | Recall | 0.705 |
| >66% | Neutral | Precision | 0.858 |
| >66% | Neutral | F1-score | 0.774 |
| >66% | Negative | Accuracy | 0.937 |
| >66% | Negative | Recall | 0.768 |
| >66% | Negative | Precision | 0.729 |
| >66% | Negative | F1-score | 0.748 |
| >50% | Positive | Accuracy | 0.786 |
| >50% | Positive | Recall | 0.535 |
| >50% | Positive | Precision | 0.645 |
| >50% | Positive | F1-score | 0.585 |
| >50% | Neutral | Accuracy | 0.735 |
| >50% | Neutral | Recall | 0.809 |
| >50% | Neutral | Precision | 0.760 |
| >50% | Neutral | F1-score | 0.784 |
| >50% | Negative | Accuracy | 0.933 |
| >50% | Negative | Recall | 0.772 |
| >50% | Negative | Precision | 0.716 |
| >50% | Negative | F1-score | 0.743 |
Lineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
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Benchmark-system
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not publishedProvider pricing
- Context window
- Not published
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Not sourced yet
- Cloud regions
- Not tracked yet
- Lifecycle
- Tracked
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing recordProvider pricing
- Self-host
- Weights are not published
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
The original benchmark tables show this evaluated configuration’s published results. Training choices, prompts, response splits, and metrics remain attached to each table. The profile stays outside general model rankings.
LPS (FinancialPhraseBank) is a research system model. No explicit reasoning mode is documented in this profile.
Evaluated system: LPS (FinancialPhraseBank). The linked benchmark-owner report supplies the full numeric results and protocol. This profile represents that evaluated configuration; it does not transfer results to a base model or another prompt. Context limit, current API tariff, and release date are not established by this evaluation.
The original benchmark results appear in the source tables above; they remain separate from weighted benchmark slots.
Last updated October 1, 2026. Runtime fields remain blank until a sourced snapshot exists.
Questions
How does LPS (FinancialPhraseBank) perform overall in AI benchmarks?
This configuration has original benchmark results in the source tables on this profile. Every published metric retains its evaluated configuration, source precision, and protocol. These results stay outside general model rankings; they do not produce a public overall score or transfer to a base model with different settings.
Does LPS (FinancialPhraseBank) have full benchmark coverage on BenchLM?
This configuration has results in the original benchmark tables on this profile. Those tables preserve every imported source metric, but they do not fill the site’s general scoring slots. Other benchmarks remain unmeasured, and compatible configurations are required before comparing results across different reports.
What is the context window size of LPS (FinancialPhraseBank)?
LPS (FinancialPhraseBank)'s context window is not documented in a source tied to this exact model yet. The profile leaves the value unavailable instead of borrowing a limit from an earlier family member or an unverified route. Maximum output length remains a separate field.