Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use vg055/multilingual-e5-large-finetuned-IberAuTexTification2024-7030-task2-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vg055/multilingual-e5-large-finetuned-IberAuTexTification2024-7030-task2-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vg055/multilingual-e5-large-finetuned-IberAuTexTification2024-7030-task2-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vg055/multilingual-e5-large-finetuned-IberAuTexTification2024-7030-task2-v2") model = AutoModelForSequenceClassification.from_pretrained("vg055/multilingual-e5-large-finetuned-IberAuTexTification2024-7030-task2-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
multilingual-e5-large-finetuned-IberAuTexTification2024-7030-task2-v2
This model is a fine-tuned version of intfloat/multilingual-e5-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5374
- F1: 0.8460
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.5894 | 1.0 | 2571 | 0.6887 | 0.7453 |
| 0.3772 | 2.0 | 5142 | 0.5798 | 0.8074 |
| 0.2181 | 3.0 | 7713 | 0.5374 | 0.8460 |
| 0.1032 | 4.0 | 10284 | 0.8335 | 0.8390 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.13.3
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