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\dontrun{} example blocks replaced with
\donttest{} (examples require a model download and cannot
run in < 5 s; \dontrun{} retained only for
rhobots_install() which installs the torch backend).load_specter2() now correctly injects adapter
weights into all transformer layers. The previous code used a nested
replacement chain
(enc$model$encoder$layer[[i]]$adapter <- adp) which R
desugars into a [[<- write-back that can reinitialise
the submodule; weights from earlier layers were silently overwritten by
each successive iteration. Fixed by pre-fetching direct layer references
(lapply(seq_len(n_layers), \(i) ...)). SPECTER2 embeddings
now match the Python reference (cosine similarity > 0.9999).
load_hf_bert() / load_specter2() now
correctly read safetensors files named
pytorch_model.safetensors in addition to
model.safetensors.
weight_loading.R now detects ZIP-archive
.bin files (PyTorch 1.6+ format, magic bytes
PK) and emits a clear error message with conversion
instructions, in addition to the pre-existing guard for legacy pickle
format (0x80).
torch::torch_load() call updated from deprecated
map_location argument to device (renamed in
torch 0.16.3).
Default repositories for load_specter2() updated to
NetworkIsLife/specter2_base and
NetworkIsLife/specter2, which host safetensors versions of
the original allenai/specter2_base and
allenai/specter2 (the AllenAI repos only provide
.bin files that R torch cannot read directly).
hdbscan_clustering() gains
allow_single_cluster (default FALSE, matching
Python hdbscan’s default). When FALSE, EOM
never returns a single root cluster when sub-structure is present in the
condensed tree. This fixes cases where diffuse embedding spaces
(continuous sentiment data, overlapping topics) collapsed to 1 cluster.
On the validation suite, datasets that previously returned 1 cluster
(Emotion, Yelp Reviews, IMDB, Rotten Tomatoes) now match Python’s
cluster counts within ±1.
topic_quality() gains a space argument
("original" / "reduced"). When
"reduced", cohesion, separation, and silhouette are
measured in the UMAP-reduced space rather than the full encoder
embedding. This makes quality metrics much more sensitive to different
UMAP/HDBSCAN parameter choices and is the recommended setting when using
sweep_topics().
sweep_topics() gains a corresponding
quality_space argument that is forwarded to
topic_quality() (default "reduced").
hdbscan_clustering() gains a knn argument
that selects the tree data structure used during Borůvka MST
construction:
"balltree" (default) — dual-tree Borůvka with a
Ball-tree index (bounding hyperspheres; effective pruning in ≥3-D)."kdtree" — dual-tree Borůvka with a KD-tree index
(axis-aligned bounding boxes; faster to build, pruning degrades above
three dimensions)."adaptive" — pre-computed kNN graph via
dbscan::kNN(), growing k until the MST is
fully connected (guaranteed connectivity, no cap)."fixed" — same as "adaptive" but caps
k at 200 (faster)."balltree" default replaces the previous fixed-kNN
approach and matches the algorithm used by the Python
hdbscan package’s boruvka_balltree mode.
Validation on two scientific-abstract corpora (n = 3,328 and n = 9,035)
confirms Adjusted Rand Index > 0.97 and Normalised Mutual Information
> 0.98 against the Python reference.gutenbergr and plotly in
rhobots_demo().useDynLib being overwritten by roxygen2
regeneration.rhobots_demo() — Gutenberg pipeline
walkthrough.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.
Health stats visible at Monitor.