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liberta-large-upos

Token Classification
Transformers
Safetensors
bert
Generated from Trainer
Eval Results
Inference Endpoints
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liberta-large-upos
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---
library_name: transformers
license: cc-by-4.0
base_model: Goader/liberta-large
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: liberta-large-upos
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: universal_dependencies
      type: universal_dependencies
      config: uk_iu
      split: validation
      args: uk_iu
    metrics:
    - name: Precision
      type: precision
      value: 0.8100632457506624
    - name: Recall
      type: recall
      value: 0.7466487546768732
    - name: F1
      type: f1
      value: 0.7541998712736135
    - name: Accuracy
      type: accuracy
      value: 0.8675486133248327
---

<!-- 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. -->

# liberta-large-upos

This model is a fine-tuned version of [Goader/liberta-large](https://huggingface.co/Goader/liberta-large) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3346
- Precision: 0.8101
- Recall: 0.7466
- F1: 0.7542
- Accuracy: 0.8675

## 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   | 338  | 1.0412          | 0.5939    | 0.4306 | 0.4617 | 0.5790   |
| No log        | 2.0   | 676  | 0.6850          | 0.6114    | 0.5788 | 0.5745 | 0.7115   |
| No log        | 3.0   | 1014 | 0.6075          | 0.6787    | 0.6205 | 0.6241 | 0.7389   |
| No log        | 4.0   | 1352 | 0.5585          | 0.7178    | 0.6393 | 0.6425 | 0.7608   |
| No log        | 5.0   | 1690 | 0.4762          | 0.7424    | 0.6737 | 0.6874 | 0.7984   |
| No log        | 6.0   | 2028 | 0.4203          | 0.7159    | 0.6962 | 0.6946 | 0.8228   |
| No log        | 7.0   | 2366 | 0.4275          | 0.7403    | 0.7081 | 0.7028 | 0.8205   |
| No log        | 8.0   | 2704 | 0.3789          | 0.7909    | 0.7189 | 0.7282 | 0.8470   |
| No log        | 9.0   | 3042 | 0.3431          | 0.8051    | 0.7415 | 0.7484 | 0.8626   |
| No log        | 10.0  | 3380 | 0.3346          | 0.8101    | 0.7466 | 0.7542 | 0.8675   |


### Framework versions

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