{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-csebuetnlp--mt5_multilingual_xlsum","slug":"csebuetnlp--mt5_multilingual_xlsum","name":"mT5_multilingual_XLSum","type":"model","url":"https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum","page_url":"https://unfragile.ai/csebuetnlp--mt5_multilingual_xlsum","categories":["model-training"],"tags":["transformers","pytorch","mt5","text2text-generation","summarization","mT5","am","ar","az","bn","my","zh","en","fr","gu","ha","hi","ig","id","ja"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-csebuetnlp--mt5_multilingual_xlsum__cap_0","uri":"capability://text.generation.language.multilingual.abstractive.summarization.with.mt5.encoder.decoder.architecture","name":"multilingual abstractive summarization with mt5 encoder-decoder architecture","description":"Performs abstractive text summarization across 19 languages using a fine-tuned mT5 (multilingual T5) encoder-decoder transformer model. The model encodes input text through a shared multilingual encoder trained on 101 languages, then decodes abstractive summaries via a language-agnostic decoder. Uses teacher-forcing during training on XLSum dataset (1.35M+ document-summary pairs) to learn cross-lingual summarization patterns without language-specific heads.","intents":["Summarize news articles, documents, or long-form content in non-English languages without language-specific model switching","Build multilingual content curation pipelines that reduce document length while preserving semantic meaning across language boundaries","Create language-agnostic summarization APIs that handle code-switching or mixed-language inputs with a single model","Reduce inference costs by using one 580M-parameter model instead of maintaining separate monolingual summarizers"],"best_for":["teams building multilingual content platforms (news aggregators, research tools, documentation systems)","developers creating language-agnostic NLP pipelines for international organizations","researchers studying cross-lingual transfer learning in sequence-to-sequence tasks","startups with limited compute budgets needing to support 19+ languages without model multiplication"],"limitations":["Abstractive summaries may hallucinate facts not present in source text — requires fact-checking for high-stakes applications","Performance degrades on languages with minimal representation in XLSum training data (e.g., Gujarati, Hausa have <5K training examples vs English's 200K+)","Fixed maximum input length of 512 tokens; longer documents require chunking and separate summarization of chunks","No extractive summarization capability — always generates new text rather than selecting source sentences","Inference latency ~2-4 seconds per document on CPU; GPU required for production throughput >10 docs/sec","Trained on news domain; performance on technical, legal, or domain-specific documents not validated"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","transformers library 4.0+","4GB+ RAM for model loading (580M parameters)","GPU with 6GB+ VRAM recommended for batch inference"],"input_types":["raw text (UTF-8 encoded strings)","pre-tokenized text (whitespace-separated tokens)","documents up to 512 subword tokens"],"output_types":["abstractive summary text (variable length, typically 10-15% of input length)","confidence scores (beam search log-probabilities)","multiple summary candidates (via beam search with num_beams parameter)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-csebuetnlp--mt5_multilingual_xlsum__cap_1","uri":"capability://text.generation.language.language.agnostic.beam.search.decoding.with.configurable.summary.length.control","name":"language-agnostic beam search decoding with configurable summary length control","description":"Implements beam search decoding with language-agnostic length penalties and early stopping to generate variable-length summaries without language-specific constraints. Uses mT5's shared vocabulary (250K tokens) and applies beam width (default 4), length penalty, and no-repeat-ngram constraints during generation. Supports both greedy decoding (fast, lower quality) and beam search (slower, higher quality) with configurable max_length and min_length parameters.","intents":["Control summary length dynamically (e.g., 50-word executive summaries vs 200-word detailed summaries) without retraining","Generate multiple diverse summary candidates via beam search for A/B testing or user selection","Prevent repetitive or degenerate outputs through n-gram blocking and length penalties","Optimize inference speed vs quality trade-off by adjusting beam width and decoding strategy"],"best_for":["applications requiring variable-length summaries (e.g., mobile apps with space constraints vs desktop with room for detail)","systems generating multiple summary candidates for human review or ranking","production pipelines where inference latency is critical and beam_width can be reduced to 1-2"],"limitations":["Beam search adds 3-5x latency vs greedy decoding; beam_width=4 requires 4x memory for attention caches","Length penalties are heuristic-based; actual summary length may exceed max_length by 5-10% due to token-level generation","No built-in constraint for exact word count — only token-level length control","Repetition blocking (no_repeat_ngram_size) may truncate summaries prematurely on repetitive source texts"],"requires":["transformers library 4.10+ (for advanced generation_config support)","PyTorch or TensorFlow backend","GPU recommended for batch decoding with beam_width > 2"],"input_types":["tokenized input_ids (shape: [batch_size, sequence_length])","attention_mask (optional, for padding handling)","generation config parameters (max_length, min_length, num_beams, length_penalty)"],"output_types":["generated token sequences (shape: [batch_size, num_beams, max_length])","beam search scores (log-probabilities per beam)","attention weights (if output_attentions=True)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-csebuetnlp--mt5_multilingual_xlsum__cap_2","uri":"capability://text.generation.language.cross.lingual.transfer.learning.via.shared.multilingual.embedding.space","name":"cross-lingual transfer learning via shared multilingual embedding space","description":"Leverages mT5's shared 250K-token vocabulary and multilingual encoder (pre-trained on 101 languages via mC4 corpus) to enable zero-shot summarization on low-resource languages not explicitly fine-tuned on XLSum. The encoder learns language-agnostic representations where semantically similar text in different languages maps to nearby embedding vectors, allowing the decoder to generate summaries for unseen languages by interpolating learned patterns from high-resource languages (English, Arabic, Chinese).","intents":["Summarize documents in languages not included in XLSum training (e.g., Swahili, Vietnamese, Thai) with degraded but functional performance","Build language-agnostic summarization systems that scale to 100+ languages without per-language fine-tuning","Detect and handle code-switching (mixed-language) documents by leveraging shared embedding space","Reduce data annotation burden for new languages by leveraging transfer from high-resource languages"],"best_for":["organizations supporting 50+ languages with limited annotation budgets","research teams studying zero-shot cross-lingual NLP capabilities","platforms serving low-resource language communities where language-specific models are unavailable"],"limitations":["Zero-shot performance on unseen languages typically 15-25% lower ROUGE scores vs fine-tuned models","Transfer quality depends on linguistic similarity — distant language families (e.g., Basque, Hungarian) see larger performance drops","Requires high-quality pre-training on mC4 corpus; languages with minimal web representation perform worse","No explicit language identification — model assumes correct language input; mixed-language documents may produce incoherent summaries","Embedding space alignment is implicit; no explicit cross-lingual constraints during training"],"requires":["mT5 model with multilingual encoder pre-training","XLSum fine-tuning on at least 3-5 high-resource languages for effective transfer","Input text in UTF-8 encoding with correct language specification"],"input_types":["text in any of 101 languages supported by mT5 pre-training","code-switched text (mixed languages)","transliterated text (if supported by mT5 tokenizer)"],"output_types":["abstractive summaries in the same language as input","embedding vectors (if intermediate representations extracted)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-csebuetnlp--mt5_multilingual_xlsum__cap_3","uri":"capability://automation.workflow.batch.document.summarization.with.dynamic.batching.and.memory.efficient.inference","name":"batch document summarization with dynamic batching and memory-efficient inference","description":"Processes multiple documents in parallel using PyTorch/TensorFlow batching with configurable batch sizes and dynamic padding to minimize memory overhead. Implements gradient checkpointing and mixed-precision inference (FP16) to reduce memory footprint from 4GB to ~2GB while maintaining summary quality. Supports variable-length inputs within a batch by padding to the longest sequence length, with attention masks to ignore padding tokens during computation.","intents":["Summarize 100s-1000s of documents efficiently in production pipelines without OOM errors","Optimize GPU memory usage for cost-sensitive deployments (e.g., AWS Lambda, serverless inference)","Process documents with varying lengths (100-500 tokens) in a single batch without padding waste","Achieve 10-50x throughput improvement vs single-document inference through batching"],"best_for":["production systems processing high-volume document streams (news feeds, research paper repositories)","cost-optimized deployments on limited-resource hardware (edge devices, serverless platforms)","batch processing jobs with flexible latency requirements (overnight summarization runs)"],"limitations":["Batch size limited by GPU VRAM; typical max batch_size=8-16 on 12GB GPUs, 2-4 on 6GB GPUs","Dynamic padding adds 5-10% overhead vs fixed-length batches; optimal batch composition requires profiling","Mixed-precision (FP16) inference may introduce 1-2% quality degradation on edge cases due to numerical precision loss","Batching introduces latency variance; first document in batch waits for slowest document (tail latency problem)","No built-in distributed batching across multiple GPUs — requires manual data parallelism setup"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","GPU with 6GB+ VRAM for batch_size >= 4","transformers library with batch generation support","CUDA 11.0+ for mixed-precision inference"],"input_types":["batch of text documents (list of strings)","pre-tokenized batch (tensor of shape [batch_size, sequence_length])","attention masks (optional, for padding handling)"],"output_types":["batch of summaries (list of strings)","batch of generation scores (tensor of shape [batch_size])","timing metrics (tokens/sec, latency per document)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-csebuetnlp--mt5_multilingual_xlsum__cap_4","uri":"capability://code.generation.editing.language.specific.fine.tuning.and.domain.adaptation.on.custom.datasets","name":"language-specific fine-tuning and domain adaptation on custom datasets","description":"Provides a pre-trained checkpoint that can be further fine-tuned on domain-specific or language-specific datasets using standard PyTorch/TensorFlow training loops. The model's encoder-decoder architecture allows efficient transfer learning where the encoder weights are partially frozen (or trained with low learning rates) while the decoder is fine-tuned on new data. Supports both supervised fine-tuning (with reference summaries) and unsupervised domain adaptation via masked language modeling on in-domain text.","intents":["Adapt the model to domain-specific summarization (legal documents, medical abstracts, technical papers) with 100-1000 labeled examples","Fine-tune on language-specific corpora to improve performance on underrepresented languages (Gujarati, Hausa, Igbo)","Create specialized summarizers for specific content types (social media, scientific literature, financial reports) without training from scratch","Reduce fine-tuning time and data requirements by leveraging pre-trained multilingual representations"],"best_for":["organizations with domain-specific summarization needs and access to 100+ labeled examples","research teams studying domain adaptation in multilingual NLP","companies building vertical-specific products (legal tech, medical informatics) requiring high-quality summaries"],"limitations":["Fine-tuning requires labeled data (source-summary pairs); typical minimum 100-500 examples for meaningful improvement","Catastrophic forgetting risk — fine-tuning on narrow domain may degrade performance on general text","Hyperparameter tuning (learning rate, warmup steps, batch size) critical for convergence; no automatic tuning provided","Training time 2-8 hours on single GPU for 1000 examples; requires infrastructure for distributed training at scale","No built-in evaluation metrics — requires manual ROUGE/BLEU computation or external evaluation libraries"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","transformers library with Trainer API","GPU with 12GB+ VRAM for efficient fine-tuning","labeled dataset in format: {source_text, target_summary}","Python 3.7+ with standard ML libraries (numpy, pandas, scikit-learn)"],"input_types":["domain-specific text documents (UTF-8 strings)","reference summaries (gold-standard summaries for supervised learning)","unlabeled in-domain text (for unsupervised domain adaptation)"],"output_types":["fine-tuned model checkpoint (PyTorch .pt or TensorFlow SavedModel format)","training logs (loss curves, validation metrics)","domain-adapted summaries with improved relevance to target domain"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-csebuetnlp--mt5_multilingual_xlsum__cap_5","uri":"capability://data.processing.analysis.rouge.and.bertscore.evaluation.metrics.computation.for.summary.quality.assessment","name":"rouge and bertscore evaluation metrics computation for summary quality assessment","description":"Integrates with standard NLP evaluation libraries (rouge, bert-score) to compute ROUGE-1/2/L and BERTScore metrics comparing generated summaries against reference summaries. ROUGE measures n-gram overlap (precision, recall, F1) while BERTScore uses contextual embeddings from BERT to capture semantic similarity beyond surface-level word matching. Supports batch evaluation across multiple summaries with configurable metric variants (e.g., ROUGE-L with stemming).","intents":["Evaluate summarization quality on validation/test sets to track model performance across languages and domains","Compare fine-tuned models against baseline to quantify improvement from domain adaptation","Identify languages or document types where model performance degrades (e.g., low ROUGE on Gujarati)","Generate quality reports for stakeholders showing summarization effectiveness with standard NLP metrics"],"best_for":["ML engineers validating model performance during development and deployment","research teams publishing results with standard evaluation metrics","quality assurance teams monitoring summarization quality in production"],"limitations":["ROUGE metrics correlate imperfectly with human judgment; high ROUGE doesn't guarantee readable summaries","BERTScore depends on BERT model quality; performance varies across languages (English BERT >> low-resource languages)","Metrics require reference summaries; no automatic evaluation without gold-standard data","Computation is expensive — BERTScore requires forward passes through BERT for each summary pair (~500ms per pair)","Metrics don't capture factual correctness, coherence, or domain-specific quality criteria"],"requires":["rouge library (pip install rouge)","bert-score library (pip install bert-score)","reference summaries in same format as generated summaries","GPU recommended for BERTScore computation (CPU is 10x slower)"],"input_types":["generated summaries (list of strings)","reference summaries (list of strings, same length as generated)","metric configuration (e.g., use_stemmer=True for ROUGE)"],"output_types":["ROUGE scores (dict with keys: rouge1, rouge2, rougeL, each containing precision/recall/f1)","BERTScore (dict with keys: precision, recall, f1, each as list of scores per summary pair)","aggregated metrics (mean, std across all summaries)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","transformers library 4.0+","4GB+ RAM for model loading (580M parameters)","GPU with 6GB+ VRAM recommended for batch inference","transformers library 4.10+ (for advanced generation_config support)","PyTorch or TensorFlow backend","GPU recommended for batch decoding with beam_width > 2","mT5 model with multilingual encoder pre-training","XLSum fine-tuning on at least 3-5 high-resource languages for effective transfer"],"failure_modes":["Abstractive summaries may hallucinate facts not present in source text — requires fact-checking for high-stakes applications","Performance degrades on languages with minimal representation in XLSum training data (e.g., Gujarati, Hausa have <5K training examples vs English's 200K+)","Fixed maximum input length of 512 tokens; longer documents require chunking and separate summarization of chunks","No extractive summarization capability — always generates new text rather than selecting source sentences","Inference latency ~2-4 seconds per document on CPU; GPU required for production throughput >10 docs/sec","Trained on news domain; performance on technical, legal, or domain-specific documents not validated","Beam search adds 3-5x latency vs greedy decoding; beam_width=4 requires 4x memory for attention caches","Length penalties are heuristic-based; actual summary length may exceed max_length by 5-10% due to token-level generation","No built-in constraint for exact word count — only token-level length control","Repetition blocking (no_repeat_ngram_size) may truncate summaries prematurely on repetitive source texts","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5313744245456705,"quality":0.22,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:54.515Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":56827,"model_likes":325}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=csebuetnlp--mt5_multilingual_xlsum","compare_url":"https://unfragile.ai/compare?artifact=csebuetnlp--mt5_multilingual_xlsum"}},"signature":"kK5HMMMAHhEvI0kcEM8S96239Q3GhekMx5QtDk5WSMKF1WRipurilnBXdy3HhmDsQ8+VrkjHG6qGINFFsOFZCg==","signedAt":"2026-06-21T22:59:06.344Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/csebuetnlp--mt5_multilingual_xlsum","artifact":"https://unfragile.ai/csebuetnlp--mt5_multilingual_xlsum","verify":"https://unfragile.ai/api/v1/verify?slug=csebuetnlp--mt5_multilingual_xlsum","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}