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_RSE_v2")
model = AutoModelForSequenceClassification.from_pretrained("Ludo33/e5_RSE_v2")Quick Links
e5_RSE_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: 0.3621
- Accuracy: 0.9074
- F1: 0.9059
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
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 2.0336 | 1.0 | 69 | 1.0876 | 0.7332 | 0.7067 |
| 1.0637 | 2.0 | 138 | 0.3396 | 0.8757 | 0.8747 |
| 0.2544 | 3.0 | 207 | 0.3081 | 0.9047 | 0.9035 |
| 0.1172 | 4.0 | 276 | 0.3542 | 0.9056 | 0.9047 |
| 0.0868 | 5.0 | 345 | 0.3621 | 0.9074 | 0.9059 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
- Downloads last month
- 1
Model tree for Ludo33/e5_RSE_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_RSE_v2")