Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("DPhO05/my-roberta-RQ3")
model = AutoModelForSequenceClassification.from_pretrained("DPhO05/my-roberta-RQ3")Quick Links
my-roberta-RQ3
This model is a fine-tuned version of FacebookAI/roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4273
- Accuracy: 0.9480
- F1 Macro: 0.6383
- F1 Weighted: 0.9479
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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
|---|---|---|---|---|---|---|
| 0.3765 | 1.0 | 539 | 0.3696 | 0.9394 | 0.3048 | 0.9294 |
| 0.3333 | 2.0 | 1078 | 0.3297 | 0.9473 | 0.4082 | 0.9423 |
| 0.2892 | 3.0 | 1617 | 0.3122 | 0.9499 | 0.4966 | 0.9468 |
| 0.2429 | 4.0 | 2156 | 0.3167 | 0.9534 | 0.6575 | 0.9518 |
| 0.1842 | 5.0 | 2695 | 0.3338 | 0.9476 | 0.6299 | 0.9480 |
| 0.1577 | 6.0 | 3234 | 0.3656 | 0.9513 | 0.6632 | 0.9512 |
| 0.1413 | 7.0 | 3773 | 0.3701 | 0.9494 | 0.6440 | 0.9494 |
| 0.1064 | 8.0 | 4312 | 0.4169 | 0.9489 | 0.6454 | 0.9492 |
| 0.1277 | 9.0 | 4851 | 0.4233 | 0.9480 | 0.6350 | 0.9475 |
| 0.0979 | 10.0 | 5390 | 0.4273 | 0.9480 | 0.6383 | 0.9479 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
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Model tree for DPhO05/my-roberta-RQ3
Base model
FacebookAI/roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DPhO05/my-roberta-RQ3")