Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use tybys/bert-base-banking77-pt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tybys/bert-base-banking77-pt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tybys/bert-base-banking77-pt2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tybys/bert-base-banking77-pt2") model = AutoModelForSequenceClassification.from_pretrained("tybys/bert-base-banking77-pt2", 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 [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 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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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3116
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- F1: 0.9249
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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| 1.0321 | 1.0 | 626 | 0.7737 | 0.8426 |
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| 0.3579 | 2.0 | 1252 | 0.3710 | 0.9202 |
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| 0.1785 | 3.0 | 1878 | 0.3116 | 0.9249 |
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### Framework versions
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