# facebookresearch/LAMA

LAnguage Model Analysis

Repository: https://github.com/facebookresearch/LAMA
Canonical: https://ross.abutalabs.com/products/facebookresearch-lama
Language: Python
License: NOASSERTION
License Family: other
Archived: true
Last push: 2024-07-07T07:13:09+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2714, "days_push": 787, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1390, forks 186 (observed 2026-08-28T04:04:35.565170+00:00)

## What it is
LAMA is a research probe for analyzing the factual and commonsense knowledge stored in pretrained language models such as BERT, RoBERTa, GPT, ELMo, and Transformer-XL. It provides a unified interface to query these models with cloze-style probes and includes datasets like LAMA-UHN and Negated-LAMA.

## Use cases
- measure how much factual knowledge a pretrained language model contains
- probe BERT or RoBERTa for commonsense and world knowledge
- evaluate how context affects a language model's factual predictions
- test whether language models handle negated statements
- create the harder LAMA-UHN subset for knowledge probing
- extract sentence embeddings from pretrained models for downstream tasks

## When to choose
- you are doing NLP research on what knowledge language models encode
- you need a standardized benchmark to compare knowledge across multiple pretrained models
- you want to reproduce the 'Language Models as Knowledge Bases?' experiments

## When to avoid
- you need a production tool for serving or fine-tuning language models
- you want to probe modern LLMs like GPT-3 or Llama, which are not supported
- you need a maintained library with active support - the codebase is a research artifact with an old Python 3.7 setup

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, machine-learning, benchmarking, testing
- domain: machine-learning
- platform: python
- tags: language-models, knowledge-probing, research, pretrained-models, factual-knowledge, commonsense, natural-language-processing, linux, macos

## Member repositories
- facebookresearch/LAMA (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:35.565170+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:39:38.770059+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/LAMA (fetched 2026-08-28T04:04:35.565170+00:00, sha b753e5f690f3)
- Data as of 2026-08-30T08:39:29.467469+00:00.