Instructions to use Ro551/mt5-small-GEC-spanish-LORA-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ro551/mt5-small-GEC-spanish-LORA-merged with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small") model = PeftModel.from_pretrained(base_model, "Ro551/mt5-small-GEC-spanish-LORA-merged") - Transformers
How to use Ro551/mt5-small-GEC-spanish-LORA-merged with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ro551/mt5-small-GEC-spanish-LORA-merged", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model trained and pushed to Hugging Face Hub
Browse files- README.md +4 -4
- adapter_config.json +3 -3
- adapter_model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Gleu: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Gleu |
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### Framework versions
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1016
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- Gleu: 0.7352
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Gleu |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|
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| 0.2301 | 1.0 | 11213 | 0.1175 | 0.7102 |
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| 0.1762 | 2.0 | 22426 | 0.1016 | 0.7352 |
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### Framework versions
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adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"k",
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"v",
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"wi_1",
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"
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],
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"target_parameters": null,
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"task_type": "SEQ_2_SEQ_LM",
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"wi_0",
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"q",
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"k",
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"v",
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"wi_1",
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"wo",
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"o"
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],
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"target_parameters": null,
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"task_type": "SEQ_2_SEQ_LM",
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adapter_model.safetensors
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training_args.bin
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