Instructions to use vladjr/mt5-base-mt5-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vladjr/mt5-base-mt5-full with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("vladjr/mt5-base-mt5-full") model = AutoModelForMultimodalLM.from_pretrained("vladjr/mt5-base-mt5-full") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 7
Browse files- README.md +4 -3
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 1.
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- Validation Loss: 0.
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- Epoch:
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## Model description
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| 1.2627 | 1.0222 | 4 |
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| 1.1343 | 0.9810 | 6 |
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### Framework versions
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 1.1101
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- Validation Loss: 0.9831
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- Epoch: 7
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## Model description
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| 1.2627 | 1.0222 | 4 |
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| 1.1802 | 1.0123 | 5 |
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| 1.1343 | 0.9810 | 6 |
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| 1.1101 | 0.9831 | 7 |
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### Framework versions
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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size 3866872432
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