Instructions to use vladjr/t5-base-teste2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vladjr/t5-base-teste2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vladjr/t5-base-teste2") model = AutoModelForSeq2SeqLM.from_pretrained("vladjr/t5-base-teste2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 4
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 [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.
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- Validation Loss: 0.
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- Epoch:
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## Model description
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### Framework versions
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This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0205
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- Validation Loss: 0.0146
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- Epoch: 4
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## Model description
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| 0.0536 | 0.0178 | 1 |
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| 0.0351 | 0.0191 | 2 |
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| 0.0260 | 0.0163 | 3 |
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| 0.0205 | 0.0146 | 4 |
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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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