---
license: cc-by-4.0
base_model: allegro/herbert-large-cased
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: herbert-large-cased-upos
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: universal_dependencies
      type: universal_dependencies
      config: pl_pdb
      split: validation
      args: pl_pdb
    metrics:
    - name: Precision
      type: precision
      value: 0.9876397732043457
    - name: Recall
      type: recall
      value: 0.991338336393956
    - name: F1
      type: f1
      value: 0.9894469851843459
    - name: Accuracy
      type: accuracy
      value: 0.9953262628078375
---

<!-- 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. -->

# herbert-large-cased-upos

This model is a fine-tuned version of [allegro/herbert-large-cased](https://huggingface.co/allegro/herbert-large-cased) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0398
- Precision: 0.9876
- Recall: 0.9913
- F1: 0.9894
- Accuracy: 0.9953

## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results



### Framework versions

- Transformers 4.39.3
- Pytorch 1.11.0a0+17540c5
- Datasets 2.21.0
- Tokenizers 0.15.2