---
library_name: transformers
license: apache-2.0
base_model: cis-lmu/glot500-base
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: glot500_model_en_ewt
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: universal_dependencies
      type: universal_dependencies
      config: en_ewt
      split: test
      args: en_ewt
    metrics:
    - name: Precision
      type: precision
      value: 0.9400958805311612
    - name: Recall
      type: recall
      value: 0.9420542470878327
    - name: F1
      type: f1
      value: 0.9410740449748372
    - name: Accuracy
      type: accuracy
      value: 0.9483660387746274
---

<!-- 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. -->

# glot500_model_en_ewt

This model is a fine-tuned version of [cis-lmu/glot500-base](https://huggingface.co/cis-lmu/glot500-base) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2101
- Precision: 0.9401
- Recall: 0.9421
- F1: 0.9411
- Accuracy: 0.9484

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.0519        | 1.0   | 625  | 0.2891          | 0.9291    | 0.9298 | 0.9295 | 0.9396   |
| 0.2366        | 2.0   | 1250 | 0.2101          | 0.9401    | 0.9421 | 0.9411 | 0.9484   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3