Instructions to use dtorber/bert-base-spanish-wwm-cased_K5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/bert-base-spanish-wwm-cased_K5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dtorber/bert-base-spanish-wwm-cased_K5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dtorber/bert-base-spanish-wwm-cased_K5") model = AutoModelForSequenceClassification.from_pretrained("dtorber/bert-base-spanish-wwm-cased_K5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- F1 Macro: 0.
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- F1: 0.
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- F1 Neg: 0.
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- Acc: 0.
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- Prec: 0.
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- Recall: 0.
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- Mcc: 0.
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## Model description
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### Framework versions
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- Transformers 4.
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- Pytorch 2.2.
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- Datasets 2.
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- Tokenizers 0.15.2
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5190
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- F1 Macro: 0.8753
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- F1: 0.9122
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- F1 Neg: 0.8384
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- Acc: 0.8862
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- Prec: 0.8925
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- Recall: 0.9329
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- Mcc: 0.7523
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## Model description
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### Framework versions
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- Transformers 4.37.2
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- Pytorch 2.2.0+cu121
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- Datasets 2.16.1
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- Tokenizers 0.15.2
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runs/Mar08_22-00-39_tardis/events.out.tfevents.1709931804.tardis.3055.19
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version https://git-lfs.github.com/spec/v1
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oid sha256:c762b05e1cd080a8fb256f4b9d82b621938b2ea8c4c5eafb06a5fae9b848f487
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size 699
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