Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Ludo33/e5_Dechets_v2")
model = AutoModelForSequenceClassification.from_pretrained("Ludo33/e5_Dechets_v2")Quick Links
e5_Dechets_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: 0.3676
- Accuracy: 0.9253
- F1: 0.9249
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 |
|---|---|---|---|---|---|
| No log | 1.0 | 40 | 0.8376 | 0.7854 | 0.7839 |
| 1.167 | 2.0 | 80 | 0.2302 | 0.9157 | 0.9163 |
| 0.2646 | 3.0 | 120 | 0.2260 | 0.9253 | 0.9252 |
| 0.1136 | 4.0 | 160 | 0.3146 | 0.9253 | 0.9248 |
| 0.0465 | 5.0 | 200 | 0.3676 | 0.9253 | 0.9249 |
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_Dechets_v2
Base model
intfloat/multilingual-e5-large-instruct
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ludo33/e5_Dechets_v2")