bge-large-en-v1.5 ranks higher at 52/100 vs ubuntu_osworld_file_cache at 19/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | ubuntu_osworld_file_cache | bge-large-en-v1.5 |
|---|---|---|
| Type | Dataset | Model |
| UnfragileRank | 19/100 | 52/100 |
| Adoption | 0 | 1 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 0 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Stores pre-computed file system states and execution traces from Ubuntu desktop environment interactions, enabling rapid retrieval of realistic OS-level task demonstrations without re-executing complex multi-step workflows. The dataset captures filesystem snapshots, command sequences, and state transitions from the OSWorld benchmark, allowing models to learn from cached execution patterns rather than simulating environments from scratch.
Unique: Purpose-built cache layer for OSWorld benchmark that pre-computes and stores file system states from real Ubuntu desktop interactions, eliminating the need for agents to simulate or re-execute complex multi-step OS tasks during training and evaluation
vs alternatives: Provides 1M+ cached Ubuntu task trajectories with ground-truth file states, enabling faster agent training than alternatives that require live environment simulation or synthetic task generation
Implements a structured index over cached execution traces that maps task identifiers to sequences of file system states, command outputs, and intermediate results. Enables efficient lookup of complete task trajectories or individual execution steps without scanning the entire dataset, using hierarchical indexing by task type, complexity, and execution outcome.
Unique: Hierarchical indexing strategy that maps OSWorld tasks to complete execution trajectories with per-step file system snapshots, enabling O(1) trajectory lookup and stratified sampling by task complexity, type, and success/failure outcome
vs alternatives: Faster trajectory retrieval than sequential dataset scanning, with built-in stratification for balanced sampling across task categories and difficulty levels
Converts live Ubuntu file system states (directory trees, file contents, permissions, metadata) into serialized formats suitable for storage and transmission, and reconstructs those states for agent evaluation. Uses structured representations (JSON/Protocol Buffers) to capture file hierarchies, content hashes, and system metadata while maintaining semantic equivalence for task execution validation.
Unique: Structured serialization format that captures Ubuntu file system hierarchies with content hashing and metadata preservation, enabling deterministic state reconstruction and diff-based storage optimization for multi-step task trajectories
vs alternatives: More efficient than full filesystem snapshots (tar/zip) by using content hashing and structured metadata, enabling compact storage of millions of file states while maintaining semantic equivalence for task validation
Encodes ground-truth success criteria for each cached task (file creation, content validation, permission changes, command output matching) and provides validation functions to check whether agent actions achieve those criteria. Stores expected file states, output patterns, and side effects alongside trajectories, enabling automated evaluation without manual inspection.
Unique: Encodes task-specific success criteria (file states, content patterns, permission changes) alongside cached trajectories, enabling automated validation of agent behavior against ground truth without manual inspection or environment simulation
vs alternatives: Provides structured, automatable success validation for OS tasks, eliminating manual evaluation overhead and enabling large-scale agent benchmarking with consistent, reproducible criteria
Maintains metadata about dataset version, OSWorld benchmark version, Ubuntu system configuration, and execution environment for each cached trajectory. Enables reproducibility by documenting the exact conditions under which tasks were executed, and supports dataset evolution by tracking changes to task definitions, success criteria, or file system states across versions.
Unique: Tracks dataset version, OSWorld benchmark version, Ubuntu system configuration, and execution environment metadata for each cached trajectory, enabling reproducible evaluation and transparent tracking of benchmark evolution
vs alternatives: Provides explicit provenance tracking for OS task datasets, enabling reproducibility and version-aware evaluation that alternatives lacking metadata context cannot support
Converts English text passages into 1024-dimensional dense vector embeddings using a fine-tuned BERT architecture with contrastive learning objectives. The model applies mean pooling over token representations and normalizes outputs to unit vectors, enabling efficient similarity computations via cosine distance or dot product. Trained on diverse text pairs using in-batch negatives and hard negative mining to optimize for semantic relevance across retrieval and ranking tasks.
Unique: Achieves top-tier MTEB ranking (56.9 on NDCG@10 for retrieval) through contrastive pre-training on 430M text pairs with hard negatives, then instruction-tuning on 50+ retrieval/ranking tasks — architectural choice of mean pooling + L2 normalization enables efficient batch similarity computation without query-specific fine-tuning
vs alternatives: Outperforms OpenAI's text-embedding-3-small on MTEB retrieval benchmarks while remaining fully open-source and deployable on-premise without API costs
Computes cosine similarity between pairs of embedded texts by taking the dot product of L2-normalized vectors, producing scores in range [-1, 1] where 1.0 indicates semantic equivalence. The normalization step is built into the embedding generation pipeline, allowing single-pass similarity computation without additional normalization overhead. Supports batch processing of multiple query-document pairs simultaneously for throughput optimization.
Unique: Embeddings are pre-normalized to unit vectors during generation, eliminating the need for post-hoc normalization in similarity computation — this design choice reduces latency for high-throughput ranking scenarios by ~15% compared to models requiring explicit normalization
vs alternatives: Faster similarity computation than sparse BM25 for large-scale ranking due to vector normalization baked into the model, while maintaining competitive NDCG scores on MTEB benchmarks
bge-large-en-v1.5 scores higher at 52/100 vs ubuntu_osworld_file_cache at 19/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Generates fixed-dimensional embeddings compatible with FAISS, Annoy, HNSW, and other ANN index structures for sub-linear retrieval over large document collections. The 1024-dimensional output and L2-normalization enable efficient index construction and querying; typical index sizes are 4 bytes per dimension per document. Supports both exact brute-force search and approximate methods with configurable recall-speed tradeoffs.
Unique: 1024-dimensional vectors with L2-normalization are optimized for HNSW graph construction, achieving 95%+ recall at 10ms latency on 1M-document indices — this dimensionality-normalization combination balances index size, construction time, and query latency better than higher-dimensional alternatives
vs alternatives: Smaller index footprint than OpenAI embeddings (1024 vs 1536 dims) while maintaining superior MTEB retrieval scores, reducing storage and memory costs for large-scale deployments
Provides pre-converted model weights in PyTorch, ONNX, and SafeTensors formats, enabling deployment across diverse inference runtimes without custom conversion pipelines. ONNX export includes quantization-friendly graph structures; SafeTensors format enables fast weight loading and memory-mapped access. Supports both CPU and GPU inference with automatic device selection via sentence-transformers library.
Unique: Provides SafeTensors format alongside ONNX and PyTorch, enabling secure weight loading without code execution and memory-mapped access for efficient large-model inference — architectural choice to support three formats simultaneously reduces friction for diverse deployment targets
vs alternatives: Multi-format export reduces deployment friction compared to models requiring custom conversion pipelines; SafeTensors format provides security advantages over pickle-based PyTorch checkpoints
Accepts optional instruction prefixes (e.g., 'Represent this document for retrieval:') that guide embedding generation toward specific downstream tasks without model fine-tuning. Instructions are concatenated with input text and processed through the same BERT encoder, allowing single-model deployment across retrieval, clustering, and classification tasks. Instruction tuning was performed on 50+ diverse tasks during training, enabling zero-shot adaptation to new domains.
Unique: Instruction tuning on 50+ diverse tasks enables zero-shot task adaptation without fine-tuning, allowing single-model deployment across retrieval, clustering, and classification — architectural choice to embed instructions in the input stream rather than as separate model parameters reduces deployment complexity
vs alternatives: Enables task-specific embeddings without separate models or fine-tuning, reducing deployment overhead compared to task-specific embedding models while maintaining competitive performance on MTEB benchmarks
Processes multiple text inputs simultaneously through vectorized matrix operations, achieving 10-50x throughput improvement over sequential embedding generation. Batch size is configurable (typical: 32-256) and automatically optimized based on available GPU memory. Supports dynamic batching where variable-length sequences are padded to the longest sequence in the batch, minimizing wasted computation.
Unique: Dynamic batching with automatic padding enables 10-50x throughput improvement over sequential processing while maintaining numerical consistency — architectural choice to vectorize padding and masking operations in the BERT encoder reduces per-token overhead
vs alternatives: Batch processing throughput exceeds OpenAI's embedding API (which charges per-token) by 5-10x on large corpora, enabling cost-effective offline embedding pipelines
Model includes pre-computed evaluation results on MTEB (Massive Text Embedding Benchmark) covering 56 tasks across retrieval, clustering, semantic similarity, and reranking domains. Results are published on HuggingFace model card with detailed breakdowns by task category, enabling direct comparison against 200+ alternative embedding models. Evaluation methodology is standardized and reproducible via the MTEB library.
Unique: Ranks #1 on MTEB retrieval leaderboard (56.9 NDCG@10) through instruction-tuned contrastive learning on 430M pairs — architectural choice to optimize for MTEB tasks during training enables transparent performance comparison against 200+ alternatives
vs alternatives: Achieves top MTEB ranking while remaining fully open-source, providing transparent performance comparison unavailable for proprietary APIs like OpenAI embeddings
Model is compatible with Text Embeddings Inference (TEI) server, a Rust-based inference engine optimized for embedding workloads with features like batching, quantization, and multi-GPU support. TEI automatically handles model loading, request routing, and response formatting, enabling production-grade embedding APIs without custom inference code. Supports both HTTP and gRPC interfaces.
Unique: TEI compatibility enables production-grade embedding APIs without custom inference code — architectural choice to support TEI's Rust-based engine provides 2-3x throughput improvement over Python-based servers while maintaining model compatibility
vs alternatives: TEI deployment provides higher throughput and lower latency than custom Python inference servers, enabling cost-effective embedding APIs at scale
+1 more capabilities