---
library_name: transformers
base_model: lilt-xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- token-classification
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuned-v-1
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: funsd
      type: token-classification
    metrics:
    - name: Precision
      type: precision
      value: 0.5977671451355662
    - name: Recall
      type: recall
      value: 0.5977671451355662
    - name: F1
      type: f1
      value: 0.5977671451355662
    - name: Accuracy
      type: accuracy
      value: 0.5977671451355662
---

<!-- 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. -->

# finetuned-v-1

This model is a fine-tuned version of [lilt-xlm-roberta-base](https://huggingface.co/lilt-xlm-roberta-base) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1752
- Precision: 0.5978
- Recall: 0.5978
- F1: 0.5978
- Accuracy: 0.5978

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.3986        | 0.5319 | 100  | 1.1752          | 0.5978    | 0.5978 | 0.5978 | 0.5978   |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1