Instructions to use Ridealist/xlm-roberta-base-finetuned-panx-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ridealist/xlm-roberta-base-finetuned-panx-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Ridealist/xlm-roberta-base-finetuned-panx-de")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Ridealist/xlm-roberta-base-finetuned-panx-de") model = AutoModelForTokenClassification.from_pretrained("Ridealist/xlm-roberta-base-finetuned-panx-de", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Ridealist/xlm-roberta-base-finetuned-panx-de: direct link, hf CLI and curl.
- Browser
- Download file 1.77 kB
-
https://huggingface.co/Ridealist/xlm-roberta-base-finetuned-panx-de/resolve/main/README.md
- Command line
-
hf download hf://Ridealist/xlm-roberta-base-finetuned-panx-de/README.md
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curl -L -o README.md https://huggingface.co/Ridealist/xlm-roberta-base-finetuned-panx-de/resolve/main/README.md
1.77 kB
metadata
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8643238940065961
xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset. It achieves the following results on the evaluation set:
- Loss: 0.1363
- F1: 0.8643
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: 24
- eval_batch_size: 24
- 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 | F1 |
|---|---|---|---|---|
| 0.2536 | 1.0 | 525 | 0.1531 | 0.8247 |
| 0.1243 | 2.0 | 1050 | 0.1415 | 0.8546 |
| 0.08 | 3.0 | 1575 | 0.1363 | 0.8643 |
Framework versions
- Transformers 4.33.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1