{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-microsoft--llmlingua-2-xlm-roberta-large-meetingbank","slug":"microsoft--llmlingua-2-xlm-roberta-large-meetingbank","name":"llmlingua-2-xlm-roberta-large-meetingbank","type":"model","url":"https://huggingface.co/microsoft/llmlingua-2-xlm-roberta-large-meetingbank","page_url":"https://unfragile.ai/microsoft--llmlingua-2-xlm-roberta-large-meetingbank","categories":["model-training"],"tags":["transformers","safetensors","xlm-roberta","token-classification","arxiv:2403.12968","license:mit","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-microsoft--llmlingua-2-xlm-roberta-large-meetingbank__cap_0","uri":"capability://data.processing.analysis.meeting.transcript.token.importance.classification","name":"meeting-transcript token importance classification","description":"Classifies individual tokens in meeting transcripts as important or unimportant using XLM-RoBERTa-large architecture fine-tuned on the MeetingBank dataset. The model performs sequence-level token classification by processing the entire transcript context through a 24-layer transformer encoder, then applying a classification head to each token position to predict importance scores. This enables selective compression of meeting content by identifying which tokens carry semantic weight for downstream LLM processing.","intents":["I need to compress meeting transcripts before passing them to an LLM to reduce token costs while preserving key information","I want to identify which parts of a meeting transcript are most relevant for summarization or question-answering","I need to filter out filler words, repetitions, and low-value tokens from meeting audio transcriptions"],"best_for":["teams building meeting intelligence platforms with budget constraints on LLM API calls","developers implementing context-aware compression for long-form audio transcription workflows","enterprises processing multilingual meeting content across 100+ languages"],"limitations":["Trained exclusively on meeting transcripts — performance degrades significantly on non-meeting text (emails, documents, chat)","Token-level predictions lack document-level coherence — may mark isolated tokens as important without considering broader context relevance","No built-in confidence scoring — returns hard classifications without probability estimates for downstream filtering","Fixed vocabulary from XLM-RoBERTa pretraining — out-of-vocabulary tokens from specialized meeting domains (product names, jargon) may be misclassified","Inference latency ~500-800ms for typical 2000-token meeting transcript on CPU; GPU acceleration required for real-time processing"],"requires":["Python 3.8+","transformers library 4.30.0+","torch 1.13.0+ (CPU or CUDA 11.8+)","HuggingFace Hub account for model download (or local cache)","minimum 2GB RAM for model weights (4GB+ recommended for batch processing)"],"input_types":["raw text (meeting transcript as single string)","tokenized sequences (pre-tokenized with XLM-RoBERTa tokenizer)","batch inputs (multiple transcripts up to 512 tokens each)"],"output_types":["token-level classification labels (binary: important/unimportant)","token-level logits (raw model scores before classification)","attention weights (optional, for interpretability)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--llmlingua-2-xlm-roberta-large-meetingbank__cap_1","uri":"capability://data.processing.analysis.multilingual.token.level.semantic.understanding","name":"multilingual token-level semantic understanding","description":"Leverages XLM-RoBERTa's cross-lingual transfer capabilities to understand and classify tokens across 100+ languages using a single unified model. The architecture uses shared multilingual embeddings and transformer layers trained on Common Crawl data, allowing the fine-tuned meeting classifier to generalize to non-English meeting transcripts without language-specific retraining. Token representations are contextualized through bidirectional attention, enabling the model to disambiguate polysemous words and understand language-specific importance markers.","intents":["I need to compress meeting transcripts in Spanish, French, German, and other languages using a single model","I want to identify important tokens in code-switched meetings (mixing multiple languages) without separate pipelines","I need consistent token importance scoring across multilingual enterprise meetings for fair content compression"],"best_for":["multinational enterprises with meetings across 10+ languages","global SaaS platforms offering meeting intelligence to non-English markets","developers building language-agnostic meeting compression pipelines"],"limitations":["Cross-lingual transfer quality varies by language — high-resource languages (Spanish, French, German) perform near English; low-resource languages (Tagalog, Swahili) show 5-10% performance degradation","Code-switching (mixing languages mid-sentence) not explicitly trained for — may produce inconsistent token importance across language boundaries","Tokenization assumes XLM-RoBERTa's SentencePiece vocabulary — languages with non-Latin scripts may have subword fragmentation that affects token-level predictions","No language detection built-in — requires external language identification to validate input language matches training distribution"],"requires":["Python 3.8+","transformers 4.30.0+ with XLM-RoBERTa tokenizer","torch 1.13.0+","input text must be valid UTF-8 encoded"],"input_types":["raw text in any of 100+ supported languages","mixed-language text (code-switched transcripts)","pre-tokenized sequences with XLM-RoBERTa tokenizer"],"output_types":["per-token importance classification (language-agnostic)","token-level logits across all languages","language-specific confidence scores (if using auxiliary language detection)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--llmlingua-2-xlm-roberta-large-meetingbank__cap_2","uri":"capability://data.processing.analysis.context.aware.token.importance.scoring.with.bidirectional.attention","name":"context-aware token importance scoring with bidirectional attention","description":"Performs token importance classification using bidirectional transformer attention, where each token's importance score is computed by attending to all surrounding tokens in the full meeting transcript. The model uses 24 transformer layers with multi-head attention (16 heads, 1024 hidden dimensions) to build rich contextual representations, then applies a classification head to predict token importance. This bidirectional approach enables the model to understand that a token's importance depends on its discourse role (e.g., a speaker name is important if followed by a decision, but unimportant if just introducing a comment).","intents":["I need token importance scores that account for discourse context, not just word frequency or semantic similarity","I want to preserve tokens that are important for downstream question-answering, even if they're rare or low-frequency","I need to compress meetings while maintaining coherence by understanding which tokens are critical for understanding the flow of discussion"],"best_for":["teams building meeting summarization systems that require discourse-aware compression","developers implementing retrieval-augmented generation (RAG) over meeting archives with token-level filtering","researchers studying how transformers identify important information in long-form conversational text"],"limitations":["Bidirectional attention requires processing the entire transcript at once — cannot perform streaming/online token classification on live meeting audio","Maximum sequence length of 512 tokens (XLM-RoBERTa limit) — longer meetings must be chunked, losing cross-chunk context","Attention computation is O(n²) in sequence length — processing 512-token transcripts takes ~500-800ms on CPU, scaling poorly for batch processing","No explicit modeling of speaker identity or turn-taking structure — treats all tokens equally regardless of who spoke them","Importance scores are relative to the full transcript — removing low-importance tokens may change importance rankings of remaining tokens"],"requires":["Python 3.8+","transformers 4.30.0+","torch 1.13.0+","minimum 4GB RAM for inference (8GB+ for batch processing)","GPU recommended for latency-sensitive applications (NVIDIA CUDA 11.8+ or AMD ROCm)"],"input_types":["raw meeting transcript (string, up to 512 tokens)","pre-tokenized sequences with XLM-RoBERTa tokenizer","batch of transcripts (up to 32 sequences per batch on typical GPU)"],"output_types":["token-level importance labels (binary classification)","token-level logits (continuous scores 0-1)","attention weights (if extracted from intermediate layers for interpretability)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--llmlingua-2-xlm-roberta-large-meetingbank__cap_3","uri":"capability://data.processing.analysis.batch.token.classification.with.dynamic.padding","name":"batch token classification with dynamic padding","description":"Processes multiple meeting transcripts in parallel using dynamic padding, where sequences are padded to the longest length in the batch rather than a fixed maximum length. The model uses HuggingFace's DataCollator pattern to group variable-length transcripts into batches, apply padding/truncation, and generate attention masks that tell the transformer to ignore padding tokens. This enables efficient GPU utilization by minimizing wasted computation on padding while maintaining correctness of token-level predictions.","intents":["I need to classify tokens in 100+ meeting transcripts efficiently without processing them one-by-one","I want to minimize GPU memory usage when processing batches of variable-length meetings","I need to parallelize token importance scoring across a corpus of meetings for bulk compression"],"best_for":["teams processing large archives of meeting transcripts (100s-1000s) for bulk compression","data engineers building ETL pipelines that classify tokens in meeting data before storage","researchers evaluating model performance across meeting datasets with varying transcript lengths"],"limitations":["Dynamic padding requires knowing all sequence lengths before batching — incompatible with streaming/online processing","Batch size is limited by GPU memory — typical batch size 8-32 sequences on 8GB GPU, 32-64 on 24GB GPU","Padding tokens are included in computation but masked out — adds ~10-15% overhead compared to processing exact-length sequences","Attention masks must be correctly generated — incorrect masking can leak information from padding tokens into predictions","No built-in distributed processing — requires external frameworks (Ray, Spark) for multi-GPU/multi-node scaling"],"requires":["Python 3.8+","transformers 4.30.0+ with DataCollator utilities","torch 1.13.0+","GPU with 8GB+ VRAM for batch processing (CPU possible but slow: ~5-10 transcripts/minute)","HuggingFace datasets library for efficient batch loading"],"input_types":["list of meeting transcripts (variable length, up to 512 tokens each)","pre-tokenized batch tensors with attention masks","streaming data from file (JSONL, CSV) or database"],"output_types":["batch of token-level classifications (shape: [batch_size, seq_length])","batch of logits (shape: [batch_size, seq_length, num_classes])","attention weights for interpretability (optional)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--llmlingua-2-xlm-roberta-large-meetingbank__cap_4","uri":"capability://data.processing.analysis.token.importance.based.meeting.compression.with.configurable.compression.ratios","name":"token importance-based meeting compression with configurable compression ratios","description":"Enables selective compression of meeting transcripts by filtering tokens based on their importance scores, with configurable compression ratios (e.g., keep top 50% of tokens, remove bottom 50%). The model outputs importance scores for each token, which are then used to rank and filter tokens, producing a compressed transcript that retains high-importance content. This can be applied at different compression levels (aggressive: 30% of tokens, moderate: 60%, conservative: 80%) to trade off between compression and information retention.","intents":["I need to reduce token count in meeting transcripts by 40-70% before passing to an LLM to cut API costs","I want to create multiple compression levels (summary, detailed, full) from a single meeting transcript","I need to compress meetings while maintaining enough context for downstream tasks like Q&A or summarization"],"best_for":["teams using LLM APIs (OpenAI, Anthropic) for meeting analysis and wanting to reduce token costs","developers building meeting intelligence products with tiered compression options","enterprises processing large volumes of meeting transcripts with budget constraints on LLM usage"],"limitations":["Compression is lossy — removing low-importance tokens may lose context needed for specific downstream tasks (e.g., action item extraction may need speaker names that are marked unimportant)","No guarantee of grammatical coherence after token removal — compressed transcripts may have broken sentences or missing context","Compression ratio is global — cannot selectively compress certain sections (e.g., compress small talk but preserve decisions)","Token importance is relative to the full transcript — compression at different ratios may produce inconsistent results","No built-in evaluation of compression quality — requires manual validation or downstream task evaluation to assess information loss"],"requires":["Python 3.8+","transformers 4.30.0+","torch 1.13.0+","XLM-RoBERTa tokenizer for consistent tokenization","optional: evaluation metrics library (ROUGE, BERTScore) for compression quality assessment"],"input_types":["meeting transcript (raw text or pre-tokenized)","compression ratio parameter (0.0-1.0, where 0.5 = keep 50% of tokens)","optional: token importance scores from model inference"],"output_types":["compressed transcript (text with low-importance tokens removed)","compression statistics (original token count, compressed token count, compression ratio achieved)","token importance scores for each token (for debugging/analysis)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","transformers library 4.30.0+","torch 1.13.0+ (CPU or CUDA 11.8+)","HuggingFace Hub account for model download (or local cache)","minimum 2GB RAM for model weights (4GB+ recommended for batch processing)","transformers 4.30.0+ with XLM-RoBERTa tokenizer","torch 1.13.0+","input text must be valid UTF-8 encoded","transformers 4.30.0+","minimum 4GB RAM for inference (8GB+ for batch processing)"],"failure_modes":["Trained exclusively on meeting transcripts — performance degrades significantly on non-meeting text (emails, documents, chat)","Token-level predictions lack document-level coherence — may mark isolated tokens as important without considering broader context relevance","No built-in confidence scoring — returns hard classifications without probability estimates for downstream filtering","Fixed vocabulary from XLM-RoBERTa pretraining — out-of-vocabulary tokens from specialized meeting domains (product names, jargon) may be misclassified","Inference latency ~500-800ms for typical 2000-token meeting transcript on CPU; 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