---
license: mit
base_model: deepset/gbert-large
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gbert-large-upos
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: universal_dependencies
      type: universal_dependencies
      config: de_gsd
      split: validation
      args: de_gsd
    metrics:
    - name: Precision
      type: precision
      value: 0.825291976991079
    - name: Recall
      type: recall
      value: 0.7826990832215603
    - name: F1
      type: f1
      value: 0.7912197452035137
    - name: Accuracy
      type: accuracy
      value: 0.9413806706114398
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# gbert-large-upos

This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1996
- Precision: 0.8253
- Recall: 0.7827
- F1: 0.7912
- Accuracy: 0.9414

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 438  | 0.3197          | 0.8098    | 0.7291 | 0.7486 | 0.8936   |
| No log        | 2.0   | 876  | 0.2261          | 0.8287    | 0.7679 | 0.7832 | 0.9269   |
| No log        | 3.0   | 1314 | 0.1996          | 0.8253    | 0.7827 | 0.7912 | 0.9414   |
| No log        | 4.0   | 1752 | 0.2183          | 0.8162    | 0.8006 | 0.8041 | 0.9435   |
| No log        | 5.0   | 2190 | 0.2120          | 0.8198    | 0.8025 | 0.8074 | 0.9496   |
| No log        | 6.0   | 2628 | 0.2339          | 0.8207    | 0.8068 | 0.8116 | 0.9489   |
| No log        | 7.0   | 3066 | 0.2728          | 0.8156    | 0.8045 | 0.8071 | 0.9486   |
| No log        | 8.0   | 3504 | 0.2790          | 0.8205    | 0.8110 | 0.8132 | 0.9527   |
| No log        | 9.0   | 3942 | 0.2854          | 0.8306    | 0.8096 | 0.8146 | 0.9527   |
| No log        | 10.0  | 4380 | 0.2906          | 0.8299    | 0.8115 | 0.8151 | 0.9534   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1