Token Classification
GLiNER
PyTorch
English
urchade tomaarsen HF staff commited on
Commit
bc4f9ea
1 Parent(s): ece5c02

Update README with installable gliner; add library_name (#5)

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- Update README with installable gliner; add library_name (c15228d1bbf832445b3086135386037f70f7c18f)


Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>

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  1. README.md +5 -6
README.md CHANGED
@@ -5,6 +5,7 @@ language:
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  pipeline_tag: token-classification
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  datasets:
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  - Universal-NER/Pile-NER-type
 
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  ---
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  # Model Card for GLiNER-base
@@ -20,18 +21,16 @@ This version has been trained on the **Pile-NER** dataset (Research purpose). Co
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  * Repository: https://github.com/urchade/GLiNER
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  ## Installation
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- To use this model, you must download the GLiNER repository and install its dependencies:
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  ```
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- !git clone https://github.com/urchade/GLiNER.git
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- %cd GLiNER
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- !pip install -r requirements.txt
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  ```
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  ## Usage
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- Once you've downloaded the GLiNER repository, you can import the GLiNER class from the `model` file. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
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  ```python
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- from model import GLiNER
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  model = GLiNER.from_pretrained("urchade/gliner_base")
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  pipeline_tag: token-classification
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  datasets:
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  - Universal-NER/Pile-NER-type
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+ library_name: gliner
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  ---
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  # Model Card for GLiNER-base
 
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  * Repository: https://github.com/urchade/GLiNER
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  ## Installation
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+ To use this model, you must install the GLiNER Python library:
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  ```
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+ !pip install gliner
 
 
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  ```
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  ## Usage
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+ Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
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  ```python
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+ from gliner import GLiNER
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  model = GLiNER.from_pretrained("urchade/gliner_base")
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