Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/bert_Pfinal_2e-5_16_10 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_10 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_10")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/bert_Pfinal_2e-5_16_10") model = AutoModelForSequenceClassification.from_pretrained("fredymad/bert_Pfinal_2e-5_16_10", 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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This model is a fine-tuned version of [fredymad/bert_Pfinal_2e-5_16_2](https://huggingface.co/fredymad/bert_Pfinal_2e-5_16_2) on the None dataset.
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It achieves the following results on the evaluation set:
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- F1: 0.
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## Model description
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### Framework versions
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---
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tags:
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- generated_from_trainer
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metrics:
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This model is a fine-tuned version of [fredymad/bert_Pfinal_2e-5_16_2](https://huggingface.co/fredymad/bert_Pfinal_2e-5_16_2) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6295
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- F1: 0.7355
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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| 0.101 | 1.0 | 669 | 0.3000 | 0.7169 |
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| 0.1282 | 2.0 | 1338 | 0.2993 | 0.7361 |
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| 0.0548 | 3.0 | 2007 | 0.3924 | 0.7308 |
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| 0.0278 | 4.0 | 2676 | 0.4989 | 0.7221 |
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| 0.0229 | 5.0 | 3345 | 0.6089 | 0.6940 |
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| 0.0168 | 6.0 | 4014 | 0.5561 | 0.7361 |
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| 0.0082 | 7.0 | 4683 | 0.6112 | 0.7297 |
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| 0.008 | 8.0 | 5352 | 0.6101 | 0.7343 |
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| 0.0052 | 9.0 | 6021 | 0.6253 | 0.7400 |
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| 0.003 | 10.0 | 6690 | 0.6295 | 0.7355 |
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### Framework versions
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