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---
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
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