Instructions to use GuysTrans/bart-base-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GuysTrans/bart-base-mini with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("GuysTrans/bart-base-mini") model = AutoModelForSeq2SeqLM.from_pretrained("GuysTrans/bart-base-mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from GuysTrans/bart-base-mini: direct link, hf CLI and curl.
- Browser
- Download file 1.53 kB
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https://huggingface.co/GuysTrans/bart-base-mini/resolve/8eff321f886a44cd4093eb287cab8e255f08990e/README.md
- Command line
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hf download hf://GuysTrans/bart-base-mini@8eff321f886a44cd4093eb287cab8e255f08990e/README.md
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curl -L -o README.md https://huggingface.co/GuysTrans/bart-base-mini/resolve/8eff321f886a44cd4093eb287cab8e255f08990e/README.md
1.53 kB
metadata
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-base-mini
results: []
bart-base-mini
This model is a fine-tuned version of facebook/bart-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.0471
- Rouge1: 10.5206
- Rouge2: 2.2779
- Rougel: 8.3921
- Rougelsum: 10.1498
- Gen Len: 20.0
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: 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.6435 | 1.0 | 23165 | 3.0471 | 10.5206 | 2.2779 | 8.3921 | 10.1498 | 20.0 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3