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
| license: mit | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: KostiuchenkoArtem/bart_large_multi | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # KostiuchenkoArtem/bart_large_multi | |
| This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/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 | |