Instructions to use royam0820/xlm-roberta-base-finetuned-panx-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use royam0820/xlm-roberta-base-finetuned-panx-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="royam0820/xlm-roberta-base-finetuned-panx-en")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("royam0820/xlm-roberta-base-finetuned-panx-en") model = AutoModelForTokenClassification.from_pretrained("royam0820/xlm-roberta-base-finetuned-panx-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from royam0820/xlm-roberta-base-finetuned-panx-en: direct link, hf CLI and curl.
- Browser
- Download file 1.7 kB
-
https://huggingface.co/royam0820/xlm-roberta-base-finetuned-panx-en/resolve/main/README.md
- Command line
-
hf download hf://royam0820/xlm-roberta-base-finetuned-panx-en/README.md
-
curl -L -o README.md https://huggingface.co/royam0820/xlm-roberta-base-finetuned-panx-en/resolve/main/README.md
1.7 kB
| license: mit | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - xtreme | |
| metrics: | |
| - f1 | |
| model-index: | |
| - name: xlm-roberta-base-finetuned-panx-en | |
| results: | |
| - task: | |
| name: Token Classification | |
| type: token-classification | |
| dataset: | |
| name: xtreme | |
| type: xtreme | |
| args: PAN-X.en | |
| metrics: | |
| - name: F1 | |
| type: f1 | |
| value: 0.6886160714285715 | |
| <!-- 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-base-finetuned-panx-en | |
| This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4043 | |
| - F1: 0.6886 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | 1.1347 | 1.0 | 50 | 0.5771 | 0.4880 | | |
| | 0.5066 | 2.0 | 100 | 0.4209 | 0.6582 | | |
| | 0.3631 | 3.0 | 150 | 0.4043 | 0.6886 | | |
| ### Framework versions | |
| - Transformers 4.11.3 | |
| - Pytorch 1.12.0+cu113 | |
| - Datasets 1.16.1 | |
| - Tokenizers 0.10.3 | |