Instructions to use tnatvu/xlm-roberta-base-absa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tnatvu/xlm-roberta-base-absa with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, XLMRobertaForABSA tokenizer = AutoTokenizer.from_pretrained("tnatvu/xlm-roberta-base-absa") model = XLMRobertaForABSA.from_pretrained("tnatvu/xlm-roberta-base-absa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlm-roberta-base-absa
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1653
- F1: 0.6384
- F1 Macro: 0.6187
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: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | F1 Macro |
|---|---|---|---|---|---|
| 0.271 | 1.0 | 102 | 0.1841 | 0.3987 | 0.3050 |
| 0.1143 | 2.0 | 204 | 0.1321 | 0.5433 | 0.4849 |
| 0.0704 | 3.0 | 306 | 0.1464 | 0.5682 | 0.5350 |
| 0.0473 | 4.0 | 408 | 0.1463 | 0.6021 | 0.5772 |
| 0.0307 | 5.0 | 510 | 0.1571 | 0.6278 | 0.6003 |
| 0.0192 | 6.0 | 612 | 0.1653 | 0.6384 | 0.6187 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.3.0.dev20240121
- Datasets 2.16.1
- Tokenizers 0.15.0
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