---
library_name: transformers
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: 0.91656329817706
    - name: Recall
      type: recall
      value: 0.8825519391481612
    - name: F1
      type: f1
      value: 0.892780213659273
    - name: Accuracy
      type: accuracy
      value: 0.9827837758972863
---

<!-- 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: 0.0611
- Precision: 0.9166
- Recall: 0.8826
- F1: 0.8928
- Accuracy: 0.9828

## 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  | 0.2798          | 0.8362    | 0.8222 | 0.8271 | 0.8779   |
| No log        | 2.0   | 876  | 0.1613          | 0.9287    | 0.8511 | 0.8677 | 0.9240   |
| No log        | 3.0   | 1314 | 0.0967          | 0.8845    | 0.8530 | 0.8562 | 0.9539   |
| No log        | 4.0   | 1752 | 0.0917          | 0.9103    | 0.8461 | 0.8657 | 0.9629   |
| No log        | 5.0   | 2190 | 0.0782          | 0.8965    | 0.8704 | 0.8764 | 0.9666   |
| No log        | 6.0   | 2628 | 0.0766          | 0.8973    | 0.8704 | 0.8767 | 0.9691   |
| No log        | 7.0   | 3066 | 0.0634          | 0.9171    | 0.8811 | 0.8923 | 0.9790   |
| No log        | 8.0   | 3504 | 0.0626          | 0.9139    | 0.8909 | 0.8989 | 0.9796   |
| No log        | 9.0   | 3942 | 0.0675          | 0.9131    | 0.8792 | 0.8893 | 0.9803   |
| No log        | 10.0  | 4380 | 0.0611          | 0.9166    | 0.8826 | 0.8928 | 0.9828   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1