Instructions to use dusersad12/SweepBestModel-Repo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dusersad12/SweepBestModel-Repo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dusersad12/SweepBestModel-Repo")# 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") - Notebooks
- Google Colab
- Kaggle
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Download README.md from dusersad12/SweepBestModel-Repo: direct link, hf CLI and curl.
- Browser
- Download file 1.22 kB
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https://huggingface.co/dusersad12/SweepBestModel-Repo/resolve/main/README.md
- Command line
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hf download hf://dusersad12/SweepBestModel-Repo/README.md
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curl -L -o README.md https://huggingface.co/dusersad12/SweepBestModel-Repo/resolve/main/README.md
1.22 kB
metadata
license: apache-2.0
library_name: transformers
tags:
- text-classification
- roberta
- sweep
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")
Figures