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
| { | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "dtype": "float16", | |
| "eos_token_id": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "layer_norm_eps": 1e-07, | |
| "legacy": true, | |
| "max_position_embeddings": 512, | |
| "max_relative_positions": -1, | |
| "model_type": "deberta-v2", | |
| "norm_rel_ebd": "layer_norm", | |
| "num_attention_heads": 6, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0.0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 384, | |
| "pos_att_type": [ | |
| "p2c", | |
| "c2p" | |
| ], | |
| "position_biased_input": false, | |
| "position_buckets": 256, | |
| "relative_attention": true, | |
| "share_att_key": true, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.8.0", | |
| "type_vocab_size": 0, | |
| "vocab_size": 128011 | |
| } | |