Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("RantiRepo/job_classifier_xlmroberta")
model = AutoModelForSequenceClassification.from_pretrained("RantiRepo/job_classifier_xlmroberta")Quick Links
job_classifier_model_v2
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the Job dataset. It achieves the following results on the evaluation set:
- Loss: 0.0491
- Accuracy: 0.9942
- Precision: 0.9930
- Recall: 0.9953
- F1: 0.9942
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
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.1122 | 1.0 | 859 | 0.0529 | 0.9907 | 0.9930 | 0.9883 | 0.9907 |
| 0.0325 | 2.0 | 1718 | 0.0338 | 0.9953 | 0.9930 | 0.9977 | 0.9953 |
| 0.0088 | 3.0 | 2577 | 0.0491 | 0.9942 | 0.9930 | 0.9953 | 0.9942 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
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Model tree for RantiRepo/job_classifier_xlmroberta
Base model
FacebookAI/xlm-roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RantiRepo/job_classifier_xlmroberta")