Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/bert_Pfinal_4CLASES_2e-5_16_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fredymad/bert_Pfinal_4CLASES_2e-5_16_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fredymad/bert_Pfinal_4CLASES_2e-5_16_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/bert_Pfinal_4CLASES_2e-5_16_2") model = AutoModelForSequenceClassification.from_pretrained("fredymad/bert_Pfinal_4CLASES_2e-5_16_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
Browse files
README.md
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This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) 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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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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### Framework versions
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This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3365
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- Accuracy: 0.8987
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| 0.4009 | 1.0 | 669 | 0.2939 | 0.8979 |
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| 0.2618 | 2.0 | 1338 | 0.3365 | 0.8987 |
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### Framework versions
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