# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dusersad12/SweepBestModel-Repo")
model = AutoModelForSequenceClassification.from_pretrained("dusersad12/SweepBestModel-Repo", device_map="auto")Quick Links
SweepBestModel
Overview
This model was selected from a hyperparameter sweep as the best-performing run based on validation accuracy. It is a RoBERTa-based sequence classifier fine-tuned on our internal dataset.
Training Configuration
- Learning Rate: 3e-5
- Batch Size: 64
- Epochs: 10
- Best Validation Accuracy: 0.864
Benchmark Results
| Benchmark | Score |
|---|---|
| MNLI (m/mm) | 0.864 |
| SST-2 | 0.864 |
| QQP | 0.864 |
| QNLI | 0.864 |
| RTE | 0.864 |
| CoLA | 0.864 |
| STS-B | 0.864 |
| MRPC | 0.864 |
Usage
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("SweepBestModel-Repo")
tokenizer = AutoTokenizer.from_pretrained("SweepBestModel-Repo")
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dusersad12/SweepBestModel-Repo")