Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Rami/multi-label-class-classification-on-github-issues with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rami/multi-label-class-classification-on-github-issues with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rami/multi-label-class-classification-on-github-issues")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rami/multi-label-class-classification-on-github-issues") model = AutoModelForSequenceClassification.from_pretrained("Rami/multi-label-class-classification-on-github-issues") - Inference
- Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: multi-label-class-classification-on-github-issues | |
| 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. --> | |
| # multi-label-class-classification-on-github-issues | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1391 | |
| - Micro f1: 0.5005 | |
| - Macro f1: 0.0340 | |
| ## 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: 3e-05 | |
| - train_batch_size: 128 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 15 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Micro f1 | Macro f1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | |
| | No log | 1.0 | 13 | 0.3988 | 0.3783 | 0.0172 | | |
| | No log | 2.0 | 26 | 0.3288 | 0.3791 | 0.0172 | | |
| | No log | 3.0 | 39 | 0.2753 | 0.3791 | 0.0172 | | |
| | No log | 4.0 | 52 | 0.2347 | 0.3791 | 0.0172 | | |
| | No log | 5.0 | 65 | 0.2051 | 0.3791 | 0.0172 | | |
| | No log | 6.0 | 78 | 0.1849 | 0.3791 | 0.0172 | | |
| | No log | 7.0 | 91 | 0.1710 | 0.3791 | 0.0172 | | |
| | No log | 8.0 | 104 | 0.1614 | 0.3791 | 0.0172 | | |
| | No log | 9.0 | 117 | 0.1546 | 0.3791 | 0.0172 | | |
| | No log | 10.0 | 130 | 0.1497 | 0.3796 | 0.0173 | | |
| | No log | 11.0 | 143 | 0.1458 | 0.4143 | 0.0238 | | |
| | No log | 12.0 | 156 | 0.1427 | 0.4567 | 0.0295 | | |
| | No log | 13.0 | 169 | 0.1406 | 0.4709 | 0.0310 | | |
| | No log | 14.0 | 182 | 0.1391 | 0.4886 | 0.0327 | | |
| | No log | 15.0 | 195 | 0.1386 | 0.4909 | 0.0330 | | |
| ### Framework versions | |
| - Transformers 4.25.1 | |
| - Pytorch 1.13.0+cu116 | |
| - Datasets 2.8.0 | |
| - Tokenizers 0.13.2 | |