Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("phunganhsang/XMLRoberta_Dataset8kMeta")
model = AutoModelForSequenceClassification.from_pretrained("phunganhsang/XMLRoberta_Dataset8kMeta")Quick Links
XMLRoberta_Dataset8kMeta
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3777
- Accuracy: 0.9464
- F1: 0.9465
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.6260 | 200 | 0.2882 | 0.9243 | 0.9108 |
| 0.4946 | 3.2520 | 400 | 0.2445 | 0.9166 | 0.9073 |
| 0.1849 | 4.8780 | 600 | 0.2282 | 0.9447 | 0.9453 |
| 0.1256 | 6.5041 | 800 | 0.2781 | 0.9472 | 0.9459 |
| 0.0808 | 8.1301 | 1000 | 0.2434 | 0.9404 | 0.9422 |
| 0.0808 | 9.7561 | 1200 | 0.3441 | 0.9438 | 0.9436 |
| 0.0592 | 11.3821 | 1400 | 0.3017 | 0.9506 | 0.9499 |
| 0.0418 | 13.0081 | 1600 | 0.3410 | 0.9455 | 0.9446 |
| 0.0345 | 14.6341 | 1800 | 0.3515 | 0.9430 | 0.9434 |
| 0.0233 | 16.2602 | 2000 | 0.3690 | 0.9455 | 0.9458 |
| 0.0233 | 17.8862 | 2200 | 0.3746 | 0.9481 | 0.9480 |
| 0.0176 | 19.5122 | 2400 | 0.3777 | 0.9464 | 0.9465 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for phunganhsang/XMLRoberta_Dataset8kMeta
Base model
FacebookAI/xlm-roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="phunganhsang/XMLRoberta_Dataset8kMeta")