Instructions to use KostiuchenkoArtem/bart_large_multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KostiuchenkoArtem/bart_large_multi with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KostiuchenkoArtem/bart_large_multi") model = AutoModelForSeq2SeqLM.from_pretrained("KostiuchenkoArtem/bart_large_multi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from KostiuchenkoArtem/bart_large_multi: direct link, hf CLI and curl.
- Browser
- Download file 1.45 kB
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https://huggingface.co/KostiuchenkoArtem/bart_large_multi/resolve/main/README.md
- Command line
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hf download hf://KostiuchenkoArtem/bart_large_multi/README.md
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curl -L -o README.md https://huggingface.co/KostiuchenkoArtem/bart_large_multi/resolve/main/README.md
1.45 kB
metadata
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: KostiuchenkoArtem/bart_large_multi
results: []
KostiuchenkoArtem/bart_large_multi
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 1.4183
- Validation Loss: 2.2949
- Epoch: 3
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 2.2305 | 2.1257 | 0 |
| 1.9043 | 2.1163 | 1 |
| 1.6451 | 2.1815 | 2 |
| 1.4183 | 2.2949 | 3 |
Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3