|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- xtreme_en |
|
metrics: |
|
- accuracy |
|
- f1 |
|
widget: |
|
- text: "My name is Julia, I study at Imperial College, in London" |
|
example_title: "Example 1" |
|
- text: "My name is Sarah and I live in Paris" |
|
example_title: "Example 2" |
|
- text: "My name is Clara and I live in Berkeley, California" |
|
example_title: "Example 3" |
|
model-index: |
|
- name: XLM-RoBERTa-xtreme-en |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: xtreme_en |
|
type: xtreme_en |
|
args: default |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9109484079686702 |
|
- name: F1 |
|
type: f1 |
|
value: 0.7544312444026322 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# XLM-RoBERTa-xtreme-en |
|
|
|
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme_en dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2838 |
|
- Accuracy: 0.9109 |
|
- F1: 0.7544 |
|
|
|
## 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: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
|
| 0.6502 | 1.0 | 235 | 0.3328 | 0.8995 | 0.7251 | |
|
| 0.3239 | 2.0 | 470 | 0.2897 | 0.9101 | 0.7473 | |
|
| 0.2644 | 3.0 | 705 | 0.2838 | 0.9109 | 0.7544 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- Pytorch 1.11.0+cu113 |
|
- Datasets 2.3.2 |
|
- Tokenizers 0.12.1 |
|
|
