Instructions to use rb05751/bart_fine_tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rb05751/bart_fine_tuned with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rb05751/bart_fine_tuned") model = AutoModelForSeq2SeqLM.from_pretrained("rb05751/bart_fine_tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from rb05751/bart_fine_tuned: direct link, hf CLI and curl.
- Browser
- Download file 1.36 kB
-
https://huggingface.co/rb05751/bart_fine_tuned/resolve/main/README.md
- Command line
-
hf download hf://rb05751/bart_fine_tuned/README.md
-
curl -L -o README.md https://huggingface.co/rb05751/bart_fine_tuned/resolve/main/README.md
1.36 kB
metadata
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: bart_fine_tuned
results: []
bart_fine_tuned
This model is a fine-tuned version of facebook/bart-large-cnn on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 0.1345
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: 1e-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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0858 | 1.0 | 148 | 0.1348 |
| 0.0784 | 2.0 | 296 | 0.1345 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3