Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Ludo33/e5_Biodiversite_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ludo33/e5_Biodiversite_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ludo33/e5_Biodiversite_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ludo33/e5_Biodiversite_v2") model = AutoModelForSequenceClassification.from_pretrained("Ludo33/e5_Biodiversite_v2") - Notebooks
- Google Colab
- Kaggle
e5_Biodiversite_v2
This model is a fine-tuned version of intfloat/multilingual-e5-large-instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3241
- Accuracy: 0.9315
- F1: 0.9313
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.8585 | 1.0 | 57 | 1.1899 | 0.7249 | 0.7193 |
| 0.8167 | 2.0 | 114 | 0.2690 | 0.9006 | 0.9000 |
| 0.2562 | 3.0 | 171 | 0.2405 | 0.9160 | 0.9160 |
| 0.1603 | 4.0 | 228 | 0.2849 | 0.9138 | 0.9136 |
| 0.0838 | 5.0 | 285 | 0.2489 | 0.9227 | 0.9223 |
| 0.0574 | 6.0 | 342 | 0.3241 | 0.9315 | 0.9313 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
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Model tree for Ludo33/e5_Biodiversite_v2
Base model
intfloat/multilingual-e5-large-instruct