Text Classification
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roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bradmin/reward-bert-duplicate-answer-300")
model = AutoModelForSequenceClassification.from_pretrained("bradmin/reward-bert-duplicate-answer-300")Quick Links
reward-bert-duplicate-answer-300
This model is a fine-tuned version of klue/roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2419
- Accuracy: 0.0
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: 9e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 2023
- gradient_accumulation_steps: 10
- total_train_batch_size: 60
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5015 | 0.17 | 100 | 0.5284 | 0.0 |
| 0.4259 | 0.34 | 200 | 0.3848 | 0.0 |
| 0.3808 | 0.51 | 300 | 0.2962 | 0.0 |
| 0.3328 | 0.69 | 400 | 0.2592 | 0.0 |
| 0.2086 | 0.86 | 500 | 0.2419 | 0.0 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for bradmin/reward-bert-duplicate-answer-300
Base model
klue/roberta-large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bradmin/reward-bert-duplicate-answer-300")