Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/bert_Pfinal_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_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_2e-5_16_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/bert_Pfinal_2e-5_16_2") model = AutoModelForSequenceClassification.from_pretrained("fredymad/bert_Pfinal_2e-5_16_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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# bert_Pfinal_2e-5_16_2
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-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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- F1: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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### Framework versions
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tags:
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- generated_from_trainer
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metrics:
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# bert_Pfinal_2e-5_16_2
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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: 0.2511
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- F1: 0.7361
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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| 0.2427 | 1.0 | 669 | 0.1770 | 0.7300 |
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| 0.1739 | 2.0 | 1338 | 0.2511 | 0.7361 |
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### Framework versions
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