---
license: apache-2.0
language:
- en
library_name: gliner
pipeline_tag: token-classification
---

# GLiNER-Large (Reproduce) Model

This model is a reproduce version of GLiNER-large, the training hyperparameters are different from the original model.

# Hyperparameters

The detail of training hyperparameters can see in `deberta.yaml`.

Except for config in `deberta.yaml`, i manually set the `lr_scheduler_type` to `cosine_with_min_lr` and `lr_scheduler_kwargs` to `{"min_lr_rate": 0.01}` in `train.py`:

```
    training_args = TrainingArguments(
        ...
        lr_scheduler_type="cosine_with_min_lr",
        lr_scheduler_kwargs={"min_lr_rate": 0.01},
        ...
    )
```

NOTE: The result is not stable, i guess the random shuffle of the dataset is the reason.

# Weights

Here are two weights, one is the final model after 4k iterations, which has the best performance on the zero-shot evaluation, and the other is the model after full training.


|   Model    |                                 link                                  |  AI   | literature | music | politics | science | movie | restaurant | Average |
| :--------: | :-------------------------------------------------------------------: | :---: | :--------: | :---: | :------: | :-----: | :---: | :--------: | :-----: |
| iter_4000  | [🤗](https://huggingface.co/liuyanyi/gliner_large_reproduce_iter_4000) | 56.7  |    65.1    | 69.6  |   74.2   |  60.9   | 60.6  |    39.7    |  61.0   |
| iter_10000 |      [🤗](https://huggingface.co/liuyanyi/gliner_large_reproduce)      | 55.1  |    62.9    | 68.3  |   71.6   |  57.3   | 58.4  |    40.5    |  59.2   |
|   Paper    |                  [🤗](https://huggingface.co/urchade)                  | 57.2  |    64.4    | 69.6  |   72.6   |  62.6   | 57.2  |    42.9    |  60.9   |


# Using repo
See https://github.com/urchade/GLiNER