Token Classification
GLiNER
PyTorch
multilingual
NER
GLiNER
information extraction
encoder
entity recognition
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@@ -77,11 +77,15 @@ European Championship => competitions
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  If you want to use flash attention or increase sequence length, please, check the following code:
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  ```python
 
 
 
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  model = GLiNER.from_pretrained("knowledgator/gliner-llama-1B-v1.0",
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  _attn_implementation = 'flash_attention_2',
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- max_len = 2048).to('cuda:0')
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  ```
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  ### Benchmarks
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  Below you can see the table with benchmarking results on various named entity recognition datasets:
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  If you want to use flash attention or increase sequence length, please, check the following code:
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  ```python
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+ from gliner import GLiNER
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+ import torch
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+
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  model = GLiNER.from_pretrained("knowledgator/gliner-llama-1B-v1.0",
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  _attn_implementation = 'flash_attention_2',
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+ max_len = 2048).to('cuda:0', dtype=torch.float16)
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  ```
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+
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  ### Benchmarks
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  Below you can see the table with benchmarking results on various named entity recognition datasets:
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