Transformers
Safetensors
GLiNER2
multilingual
English
Text classification
Named Entity Recognition
Relation Extraction
Intent classification
Sentiment Analysis
Topic classification
Structured extraction
Json extraction
information-extraction
boundary-extraction
schema-extraction
Instructions to use fastino/gliner2.5-multi-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fastino/gliner2.5-multi-v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import Gliner2ForSchemaExtraction model = Gliner2ForSchemaExtraction.from_pretrained("fastino/gliner2.5-multi-v1", device_map="auto") - GLiNER2
How to use fastino/gliner2.5-multi-v1 with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("fastino/gliner2.5-multi-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
Download tokenizer_config.json from fastino/gliner2.5-multi-v1: direct link, hf CLI and curl.
- Browser
- Download file 645 Bytes
-
https://huggingface.co/fastino/gliner2.5-multi-v1/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://fastino/gliner2.5-multi-v1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/fastino/gliner2.5-multi-v1/resolve/main/tokenizer_config.json
645 Bytes
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "extra_special_tokens": [ | |
| "[SEP_STRUCT]", | |
| "[SEP_TEXT]", | |
| "[P]", | |
| "[C]", | |
| "[E]", | |
| "[R]", | |
| "[L]", | |
| "[EXAMPLE]", | |
| "[OUTPUT]", | |
| "[DESCRIPTION]" | |
| ], | |
| "is_local": true, | |
| "local_files_only": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "split_by_punct": false, | |
| "tokenizer_class": "DebertaV2Tokenizer", | |
| "unk_id": 3, | |
| "unk_token": "[UNK]", | |
| "vocab_type": "spm" | |
| } | |