Instructions to use a98zhang/roberta-based_mclass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use a98zhang/roberta-based_mclass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="a98zhang/roberta-based_mclass")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("a98zhang/roberta-based_mclass") model = AutoModelForSequenceClassification.from_pretrained("a98zhang/roberta-based_mclass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from a98zhang/roberta-based_mclass: direct link, hf CLI and curl.
- Browser
- Download file 1.04 kB
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https://huggingface.co/a98zhang/roberta-based_mclass/resolve/main/README.md
- Command line
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hf download hf://a98zhang/roberta-based_mclass/README.md
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curl -L -o README.md https://huggingface.co/a98zhang/roberta-based_mclass/resolve/main/README.md
1.04 kB
metadata
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-based_mclass
results: []
roberta-based_mclass
This model is a fine-tuned version of roberta-base on the None dataset.
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: 5e-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: 3.0
Training results
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0