Token Classification
GLiNER2
Safetensors
English
extractor
Text classification
Named Entity Recognition
Relation Extraction
Intent classification
Sentiment Analysis
Topic classification
Structured extraction
Json extraction
information-extraction
boundary-extraction
Instructions to use fastino/gliner2.5-small-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use fastino/gliner2.5-small-v1 with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("fastino/gliner2.5-small-v1") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
File size: 3,152 Bytes
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"architecture": "boundary",
"architecture_version": 1,
"architectures": [
"BoundaryExtractor"
],
"attn_implementation": "sdpa",
"boundary_head": {
"abstention_loss_weight": 0.2,
"abstention_threshold": 0.5,
"adaptive_threshold": false,
"bidirectional_proposals": true,
"boundary_attention_heads": 4,
"boundary_attention_layers": 2,
"boundary_attention_window": 128,
"boundary_dim": 128,
"boundary_ffn_multiplier": 2.0,
"boundary_focal_clip": 0.05,
"boundary_focal_gamma_negative": 2.0,
"boundary_focal_gamma_positive": 0.0,
"boundary_marginal_loss": "asymmetric_focal",
"boundary_negative_weight": 0.5,
"boundary_refinement_layers": 1,
"boundary_top_k_alpha": 0.08,
"boundary_top_k_bucket": 8,
"boundary_top_k_max": 128,
"candidate_attention_heads": 4,
"candidate_attention_layers": 0,
"candidate_budget": 192,
"candidate_pool": "shared",
"classification_loss_weight": 1.0,
"classification_temperature": 1.0,
"consistency_loss_weight": 0.1,
"consistency_warmup_steps": 2000,
"content_dim": 64,
"content_soft_max_pool": false,
"count_loss_weight": 0.2,
"directional_relation_states": true,
"dropout": 0.1,
"enable_abstention": true,
"enable_count_head": true,
"enable_records": true,
"enable_relations": true,
"enable_rotary_endpoints": true,
"enable_span_content": true,
"end_block_size": 256,
"end_top_k": 24,
"endpoint_difference_features": true,
"ends_per_start": 12,
"export_mode": "auto",
"hard_negative_keep_all_when_absent": true,
"hard_negatives_per_positive": 20,
"loss_reduction": "sum",
"max_gold_per_query": 64,
"max_negative_queries_per_batch": 64,
"min_pool_per_query": 8,
"minimum_hard_negatives": 16,
"multihead_pair_compat_heads": 8,
"negative_query_ratio": 1.0,
"overlap_policy": "flat",
"pair_dim": 128,
"pair_temperature": 1.0,
"pool_boundary_top_k": 32,
"pool_size": 192,
"proposal_loss_weight": 0.3,
"query_attention_layers": 0,
"query_conditioned_inside_weight": true,
"record_anchor_proposal_threshold": 0.2,
"record_anchor_threshold": 0.5,
"record_dim": 128,
"record_field_threshold": 0.5,
"record_instance_queries": 32,
"record_loss_weight": 1.0,
"record_temperature": 1.0,
"relation_argument_proposal_threshold": 0.2,
"relation_biaffine_content": true,
"relation_heads_per_type": 32,
"relation_loss_weight": 1.0,
"relation_pair_cap": 64,
"relation_tails_per_type": 32,
"relation_temperature": 1.0,
"rerank_listwise_weight": 0.3,
"reranker_endpoint_compat": true,
"rotary_base": 10000.0,
"soft_iou_anneal_steps": 20000,
"soft_iou_aux_weight": 0.2,
"start_top_k": 24,
"starts_per_end": 12,
"training_candidate_budget": 192,
"use_inside_evidence": true,
"vectorized_pair_elements": 16777216
},
"config_version": 3,
"max_len": 4096,
"model_name": "microsoft/deberta-v3-xsmall",
"model_type": "extractor",
"token_pooling": "first",
"transformers_version": "5.8.0"
}
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