Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("emmajin0210/fine_tuned_main_raid")
model = AutoModelForSequenceClassification.from_pretrained("emmajin0210/fine_tuned_main_raid", device_map="auto")Quick Links
fine_tuned_main_raid
This model is a fine-tuned version of FacebookAI/roberta-large on the RAID dataset. It achieves the following results on the evaluation set:
- Loss: 0.4104
- Accuracy: 0.9413
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 adamw_torch 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 |
|---|---|---|---|---|
| 0.4311 | 0.0010 | 100 | 0.1732 | 0.9445 |
| 0.2372 | 0.0020 | 200 | 0.1967 | 0.9550 |
| 0.2263 | 0.0029 | 300 | 0.2582 | 0.9531 |
| 0.1791 | 0.0039 | 400 | 0.4104 | 0.9413 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for emmajin0210/fine_tuned_main_raid
Base model
FacebookAI/roberta-large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emmajin0210/fine_tuned_main_raid")