Text Classification
Transformers
PyTorch
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use franfj/DIPROMATS_subtask_1_base_train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use franfj/DIPROMATS_subtask_1_base_train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="franfj/DIPROMATS_subtask_1_base_train")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("franfj/DIPROMATS_subtask_1_base_train") model = AutoModelForSequenceClassification.from_pretrained("franfj/DIPROMATS_subtask_1_base_train") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - f1 | |
| model-index: | |
| - name: DIPROMATS_subtask_1_base_train | |
| results: [] | |
| <!-- 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. --> | |
| # DIPROMATS_subtask_1_base_train | |
| This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5120 | |
| - F1: 0.8267 | |
| ## 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: 64 | |
| - eval_batch_size: 64 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | 0.4533 | 1.0 | 182 | 0.3471 | 0.7932 | | |
| | 0.1763 | 2.0 | 364 | 0.3473 | 0.8116 | | |
| | 0.1359 | 3.0 | 546 | 0.3887 | 0.8144 | | |
| | 0.1728 | 4.0 | 728 | 0.4311 | 0.8147 | | |
| | 0.1519 | 5.0 | 910 | 0.4881 | 0.8236 | | |
| | 0.0085 | 6.0 | 1092 | 0.5120 | 0.8267 | | |
| | 0.1828 | 7.0 | 1274 | 0.5591 | 0.8118 | | |
| | 0.0071 | 8.0 | 1456 | 0.6079 | 0.8263 | | |
| | 0.0015 | 9.0 | 1638 | 0.6919 | 0.8235 | | |
| | 0.0241 | 10.0 | 1820 | 0.6990 | 0.8221 | | |
| ### Framework versions | |
| - Transformers 4.28.1 | |
| - Pytorch 1.13.1 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.3 | |