Instructions to use SaiPavanKumarMeruga/roberta-base-lora-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SaiPavanKumarMeruga/roberta-base-lora-text-classification with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("roberta-base") model = PeftModel.from_pretrained(base_model, "SaiPavanKumarMeruga/roberta-base-lora-text-classification") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from SaiPavanKumarMeruga/roberta-base-lora-text-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.74 kB
-
https://huggingface.co/SaiPavanKumarMeruga/roberta-base-lora-text-classification/resolve/main/README.md
- Command line
-
hf download hf://SaiPavanKumarMeruga/roberta-base-lora-text-classification/README.md
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curl -L -o README.md https://huggingface.co/SaiPavanKumarMeruga/roberta-base-lora-text-classification/resolve/main/README.md
1.74 kB
metadata
base_model: roberta-base
library_name: peft
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: roberta-base-lora-text-classification
results: []
roberta-base-lora-text-classification
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8264
- Accuracy: {'accuracy': 0.862}
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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- 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 | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 250 | 0.4008 | {'accuracy': 0.82} |
| 0.6294 | 2.0 | 500 | 0.6326 | {'accuracy': 0.84} |
| 0.6294 | 3.0 | 750 | 0.6141 | {'accuracy': 0.87} |
| 0.3802 | 4.0 | 1000 | 1.2677 | {'accuracy': 0.82} |
| 0.3802 | 5.0 | 1250 | 0.8264 | {'accuracy': 0.862} |
Framework versions
- PEFT 0.12.0
- Transformers 4.42.4
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1