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("SG1123/boolq_model")
model = AutoModelForSequenceClassification.from_pretrained("SG1123/boolq_model")Quick Links
boolq_model
This model is a fine-tuned version of roberta-base on the super_glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.9634
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6143 | 1.0 | 1179 | 0.5687 |
| 0.4884 | 2.0 | 2358 | 0.4964 |
| 0.3786 | 3.0 | 3537 | 0.5867 |
| 0.3378 | 4.0 | 4716 | 0.8469 |
| 0.3052 | 5.0 | 5895 | 0.9634 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for SG1123/boolq_model
Base model
FacebookAI/roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SG1123/boolq_model")