|
--- |
|
license: mit |
|
base_model: xlm-roberta-large |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- uner_cro_set |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: uner_cro_set |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: uner_cro_set |
|
type: uner_cro_set |
|
config: default |
|
split: validation |
|
args: default |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.9337152209492635 |
|
- name: Recall |
|
type: recall |
|
value: 0.9360131255127153 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9348627611634575 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9921047909563969 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# uner_cro_set |
|
|
|
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the uner_cro_set dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0482 |
|
- Precision: 0.9337 |
|
- Recall: 0.9360 |
|
- F1: 0.9349 |
|
- Accuracy: 0.9921 |
|
|
|
## 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: 3e-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 5.0 |
|
|
|
### Training results |
|
|
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.31.0 |
|
- Pytorch 1.10.1+cu113 |
|
- Datasets 2.14.4 |
|
- Tokenizers 0.13.3 |
|
|
