Instructions to use adity12345/roberta-classifier_batch16_3dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adity12345/roberta-classifier_batch16_3dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="adity12345/roberta-classifier_batch16_3dataset")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("adity12345/roberta-classifier_batch16_3dataset") model = AutoModelForSequenceClassification.from_pretrained("adity12345/roberta-classifier_batch16_3dataset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from adity12345/roberta-classifier_batch16_3dataset: direct link, hf CLI and curl.
- Browser
- Download file 1.44 kB
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https://huggingface.co/adity12345/roberta-classifier_batch16_3dataset/resolve/main/README.md
- Command line
-
hf download hf://adity12345/roberta-classifier_batch16_3dataset/README.md
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curl -L -o README.md https://huggingface.co/adity12345/roberta-classifier_batch16_3dataset/resolve/main/README.md
1.44 kB
metadata
library_name: transformers
license: mit
base_model: microsoft/deberta-large-mnli
tags:
- generated_from_trainer
model-index:
- name: roberta-classifier_batch16_3dataset
results: []
roberta-classifier_batch16_3dataset
This model is a fine-tuned version of microsoft/deberta-large-mnli on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.6920
- eval_Accuracy: 0.525
- eval_AUC: 0.498
- eval_runtime: 78.0994
- eval_samples_per_second: 37.004
- eval_steps_per_second: 2.318
- epoch: 3.0
- step: 2169
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1