Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use aomar85/M3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aomar85/M3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aomar85/M3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aomar85/M3") model = AutoModelForSequenceClassification.from_pretrained("aomar85/M3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from aomar85/M3: direct link, hf CLI and curl.
- Browser
- Download file 1.82 kB
-
https://huggingface.co/aomar85/M3/resolve/main/README.md
- Command line
-
hf download hf://aomar85/M3/README.md
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curl -L -o README.md https://huggingface.co/aomar85/M3/resolve/main/README.md
1.82 kB
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - precision | |
| - recall | |
| - f1 | |
| model-index: | |
| - name: fine-tuned-marBERT_latest | |
| 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. --> | |
| # fine-tuned-marBERT_latest | |
| This model is a fine-tuned version of [UBC-NLP/MARBERTv2](https://huggingface.co/UBC-NLP/MARBERTv2) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1243 | |
| - Accuracy: 0.9712 | |
| - Precision: 0.9730 | |
| - Recall: 0.9836 | |
| - F1: 0.9783 | |
| ## 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: 10 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | | |
| |:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:------:| | |
| | 0.1558 | 1.0 | 56232 | 0.1159 | 0.9693 | 0.9734 | 0.9802 | 0.9768 | | |
| | 0.1245 | 2.0 | 112464 | 0.1427 | 0.9696 | 0.9724 | 0.9817 | 0.9770 | | |
| | 0.1061 | 3.0 | 168696 | 0.1262 | 0.9716 | 0.9760 | 0.9810 | 0.9785 | | |
| | 0.0925 | 4.0 | 224928 | 0.1243 | 0.9712 | 0.9730 | 0.9836 | 0.9783 | | |
| ### Framework versions | |
| - Transformers 4.19.2 | |
| - Pytorch 1.11.0+cu113 | |
| - Datasets 2.2.2 | |
| - Tokenizers 0.12.1 | |