Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Mediocre-Judge/my_mind_classifier_XtremeDistil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mediocre-Judge/my_mind_classifier_XtremeDistil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mediocre-Judge/my_mind_classifier_XtremeDistil")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mediocre-Judge/my_mind_classifier_XtremeDistil") model = AutoModelForSequenceClassification.from_pretrained("Mediocre-Judge/my_mind_classifier_XtremeDistil", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: mit | |
| base_model: microsoft/xtremedistil-l6-h256-uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - precision | |
| - recall | |
| - f1 | |
| model-index: | |
| - name: my_mind_classifier_XtremeDistil | |
| 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. --> | |
| # my_mind_classifier_XtremeDistil | |
| This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6416 | |
| - Accuracy: 0.8097 | |
| - Precision: 0.8043 | |
| - Recall: 0.8097 | |
| - F1: 0.8070 | |
| ## 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: 64 | |
| - eval_batch_size: 64 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | |
| | 0.8188 | 1.0 | 9024 | 0.7572 | 0.7793 | 0.7733 | 0.7793 | 0.7763 | | |
| | 0.7031 | 2.0 | 18048 | 0.6416 | 0.8097 | 0.8043 | 0.8097 | 0.8070 | | |
| ### Framework versions | |
| - Transformers 4.45.1 | |
| - Pytorch 2.4.0 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.20.0 | |