Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Umesh/pulf-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Umesh/pulf-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Umesh/pulf-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Umesh/pulf-classifier") model = AutoModelForSequenceClassification.from_pretrained("Umesh/pulf-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Umesh/pulf-classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.69 kB
-
https://huggingface.co/Umesh/pulf-classifier/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://Umesh/pulf-classifier@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/Umesh/pulf-classifier/resolve/refs%2Fpr%2F1/README.md
1.69 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - recall | |
| - precision | |
| model-index: | |
| - name: pulf-classifier | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # pulf-classifier | |
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0233 | |
| - Accuracy: 0.9943 | |
| - F1-score: 0.9887 | |
| - Recall: 0.9910 | |
| - Precision: 0.9863 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Recall | Precision | | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:------:|:---------:| | |
| | 0.0243 | 1.0 | 8772 | 0.0228 | 0.9930 | 0.9861 | 0.9877 | 0.9846 | | |
| | 0.0183 | 2.0 | 17544 | 0.0243 | 0.9937 | 0.9875 | 0.9927 | 0.9825 | | |
| | 0.0124 | 3.0 | 26316 | 0.0233 | 0.9943 | 0.9887 | 0.9910 | 0.9863 | | |
| ### Framework versions | |
| - Transformers 4.27.4 | |
| - Pytorch 2.0.0+cu118 | |
| - Datasets 2.11.0 | |
| - Tokenizers 0.13.3 | |