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conflibertR 0.5.3

CRAN resubmission with review feedback addressed. No user-facing API changes. (0.5.2 was submitted with these changes but did not pass the incoming pretest; 0.5.3 additionally makes backend detection fast on machines where conda is installed.)

• Backend detection (package startup and conflibert_available()) now uses filesystem checks only and caches its result for the session. It previously ran conda env list, which can take more than 10 seconds per call on some systems; on CRAN’s Windows pretest machine that pushed two examples over the 10-second limit.
• New conflibert_available(): a cheap check for whether the ‘conflibert’ Python environment and its core modules are usable. It now guards all backend-dependent examples (they run wherever the backend is installed and are skipped elsewhere, e.g. on CRAN), and it is exported so your own scripts can degrade gracefully.
• Examples were rewritten to be self-contained and executable against the bundled datasets; \dontrun{} remains only on conflibert_install(), which installs software.
• Diverse active-learning query selection (diverse = TRUE) now starts k-means from deterministic farthest-first centers instead of seeded random starts, so it is reproducible without reading or modifying the user’s .Random.seed (the package no longer touches the global environment at all). Query selection remains reproducible; the exact clusters may differ slightly from 0.5.1.

conflibertR 0.5.1

Reproducibility

• conflibert_finetune(), conflibert_compare(), and conflibert_active_start() gained a seed argument (default 42). It seeds the classifier-head initialization, data shuffling, and dropout, so two runs with the same seed on the same hardware and package versions produce identical models and metrics. Change the seed to study run-to-run variability.
• Diverse active-learning query selection (diverse = TRUE) is now reproducible: the k-means clustering it uses is seeded from the session’s seed, and the user’s global RNG state is restored afterwards so nothing downstream is disturbed.
• Inference (conflibert_classify(), conflibert_multilabel(), conflibert_ner(), conflibert_qa()) was already deterministic (a plain forward pass with no sampling) and is unchanged.

conflibertR 0.5.0

Easier setup

• The backend is now PyTorch-only. TensorFlow and tf-keras are no longer installed by default, which makes conflibert_install() much smaller and more reliable, and removes the transformers < 4.50 version pin.
• The QA model runs on PyTorch too. Because the published checkpoint only ships TensorFlow weights, the first conflibert_qa() call converts them once and caches the PyTorch copy; environments that already have TensorFlow (any 0.4.0 install) do this transparently. Fresh installs can opt in with conflibert_install(qa = TRUE), and TensorFlow is never used after the one-time conversion.
• New conflibert_status(): a one-call diagnostic that checks the Python environment, required packages, and compute device, with specific advice when something is missing.
• Friendlier errors when the Python backend cannot load, and a startup hint when no conflibert environment is found.

Nicer output

• All inference results now print with a themed, readable layout (colored labels, probability bars, entity highlighting in the source sentence). They are still plain tibbles underneath, so all existing code ($, dplyr, [, etc.) keeps working unchanged.
• conflibert_finetune() results have a class with a clean print() method; all fields are accessed exactly as before.
• print() for active-learning sessions and loaded classifiers got the same treatment.

New plots

• New theme_conflibert() ggplot2 theme: a modern, flat look (no tick marks, hairline grids along the data axis only, bold left-aligned titles) used by all package plots and exported for your own figures. A grid argument controls which grid lines are kept.
• Every result object now plots itself, and the default views are aggregated, built for real corpus sizes:
  • classification results: texts per class with average confidence
  • NER results: entity counts by type, or the most frequent entities (type = "entities")
  • multilabel results: share of texts flagged per category (a single text gets its category probabilities instead)
  • model comparisons: ranked metric dot chart
  • fine-tune results: test-set confusion matrix
  • active-learning sessions: learning curve plus uncertainty trend
• Each is available both as base-graphics plot() (no extra packages) and as ggplot2::autoplot() (returns a customizable ggplot).

Faster and more capable inference

• conflibert_classify(), conflibert_multilabel(), and conflibert_ner() now run batched inference (one Python call per batch instead of per text), with a progress bar for large inputs and a heads-up message the first time a model is downloaded.
• conflibert_ner() gains score, start, and end columns (1-based character offsets into the input text).
• conflibert_qa() is vectorized (with recycling) and gains a details = TRUE argument returning answer, confidence score, and character span. The single-question default still returns a plain string, exactly as before.
• conflibert_compare() no longer fails when one model errors; the failed model gets an error column and the rest are kept.

Internal

• Added a testthat suite (63 tests) that runs against a mocked Python backend, so R-side logic is tested without torch or model downloads.

conflibertR 0.4.0

• Added conflibert_load() to load a saved fine-tuned classifier from disk, and a predict() method that runs batched inference returning a tidy tibble.
• Added LoRA (parameter-efficient) fine-tuning. Pass use_lora = TRUE to conflibert_finetune(), conflibert_compare(), or conflibert_active_start() to train with a low-rank adapter; the adapter is merged into the base model before saving so reloads are transparent. peft added to the Python install list.
• Added diversity-aware active learning. Pass diverse = TRUE to conflibert_active_start() to cluster top-scoring candidates in embedding space and pick one per cluster, preventing near-duplicates from dominating a batch.

conflibertR 0.3.1

• Added conflibert_active_label(): opens a Shiny gadget for labeling the current query, matching the GUI’s point-and-click experience. Requires shiny and miniUI (Suggests).

conflibertR 0.3.0

• Added an active-learning workflow for efficient labeling of unlabeled pools.
• conflibert_active_start(): train on a labeled seed and return the most uncertain samples from a pool.
• conflibert_active_next(): submit labels for the current query, retrain, and get the next batch.
• conflibert_active_save(): persist the active-learning model as a HuggingFace checkpoint.
• Session objects have print() and plot() methods for quick inspection and tracking metrics across rounds.
• Three query strategies supported: entropy, margin, least_confidence.
• Bundled active-learning example dataset available via conflibert_example("active").

conflibertR 0.2.0

• Added conflibert_finetune() for training custom classifiers on user data.
• Added conflibert_compare() for comparing multiple base models side by side.
• Added conflibert_benchmark() for evaluating the pretrained classifier against labeled data.
• Added conflibert_models() to list available base model architectures.
• Seven base models supported: ConfliBERT, BERT (uncased/cased), RoBERTa, ModernBERT, DeBERTa v3, DistilBERT.
• Added conflibert_example() to load bundled example datasets (binary and multiclass) for quick testing.

conflibertR 0.1.0

• Initial release.
• conflibert_ner(): Named Entity Recognition.
• conflibert_classify(): Binary classification (conflict vs non-conflict).
• conflibert_multilabel(): Multilabel event type classification.
• conflibert_qa(): Extractive question answering.
• conflibert_install(): One-time Python dependency setup.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.