Instructions to use zera09/bart_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zera09/bart_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zera09/bart_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zera09/bart_classification") model = AutoModelForSequenceClassification.from_pretrained("zera09/bart_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from zera09/bart_classification: direct link, hf CLI and curl.
- Browser
- Download file 1.07 kB
-
https://huggingface.co/zera09/bart_classification/resolve/main/README.md
- Command line
-
hf download hf://zera09/bart_classification/README.md
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curl -L -o README.md https://huggingface.co/zera09/bart_classification/resolve/main/README.md
1.07 kB
metadata
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: bart_classification
results: []
bart_classification
This model is a fine-tuned version of facebook/bart-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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Framework versions
- Transformers 4.41.1
- Pytorch 1.13.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1