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_Sentiment_Dechets_v2")
model = AutoModelForSequenceClassification.from_pretrained("Ludo33/e5_Sentiment_Dechets_v2")Quick Links
e5_Sentiment_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: 0.6051
- Accuracy: 0.9247
- F1: 0.9243
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 | 30 | 0.6040 | 0.8870 | 0.8872 |
| 0.763 | 2.0 | 60 | 0.3100 | 0.9100 | 0.9145 |
| 0.763 | 3.0 | 90 | 0.3297 | 0.9247 | 0.9267 |
| 0.2157 | 4.0 | 120 | 0.5087 | 0.9268 | 0.9255 |
| 0.1114 | 5.0 | 150 | 0.6237 | 0.9142 | 0.9151 |
| 0.1114 | 6.0 | 180 | 0.6051 | 0.9247 | 0.9243 |
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_Sentiment_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_Sentiment_Dechets_v2")