Token Classification
GLiNER
PyTorch
English
NER
information extraction
relation extraction
summarization
sentiment extraction
question-answering
Instructions to use knowledgator/gliner-llama-multitask-1B-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use knowledgator/gliner-llama-multitask-1B-v1.0 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("knowledgator/gliner-llama-multitask-1B-v1.0") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from knowledgator/gliner-llama-multitask-1B-v1.0: direct link, hf CLI and curl.
- Browser
- Download file 4.24 GB
-
https://huggingface.co/knowledgator/gliner-llama-multitask-1B-v1.0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://knowledgator/gliner-llama-multitask-1B-v1.0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/knowledgator/gliner-llama-multitask-1B-v1.0/resolve/main/pytorch_model.bin
4.24 GB
- Xet hash:
- ff47bd274940a875e682a9630d58331459970f4357034ed6f36be09f17aa60c7
- Size of remote file:
- 4.24 GB
- SHA256:
- 60d2f5cb12e7a23ee4a5087166daa8ac55cb2a7003f63441dcb95fc15355a2cc
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