Instructions to use luischir/bert-base-spanish-wwm-cased-finetuned-quales with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luischir/bert-base-spanish-wwm-cased-finetuned-quales with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="luischir/bert-base-spanish-wwm-cased-finetuned-quales")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("luischir/bert-base-spanish-wwm-cased-finetuned-quales") model = AutoModelForQuestionAnswering.from_pretrained("luischir/bert-base-spanish-wwm-cased-finetuned-quales", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +5 -5
- pytorch_model.bin +1 -1
README.md
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This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 1.0 | 204 | 1.
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| No log | 2.0 | 408 | 1.
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### Framework versions
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- Transformers 4.34.1
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- Pytorch 2.
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0811
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| No log | 1.0 | 204 | 1.2096 |
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| No log | 2.0 | 408 | 1.0549 |
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| 1.2094 | 3.0 | 612 | 1.0811 |
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### Framework versions
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- Transformers 4.34.1
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- Pytorch 2.1.0+cu118
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 437115494
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