Instructions to use Ponyyyy/squence_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ponyyyy/squence_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ponyyyy/squence_classification_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ponyyyy/squence_classification_model") model = AutoModelForSequenceClassification.from_pretrained("Ponyyyy/squence_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Ponyyyy/squence_classification_model: direct link, hf CLI and curl.
- Browser
- Download file 1.42 kB
-
https://huggingface.co/Ponyyyy/squence_classification_model/resolve/main/README.md
- Command line
-
hf download hf://Ponyyyy/squence_classification_model/README.md
-
curl -L -o README.md https://huggingface.co/Ponyyyy/squence_classification_model/resolve/main/README.md
1.42 kB
| license: mit | |
| base_model: roberta-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: squence_classification_model | |
| 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. --> | |
| # squence_classification_model | |
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1988 | |
| - Accuracy: 0.9516 | |
| ## 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: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.2017 | 1.0 | 1563 | 0.1441 | 0.9482 | | |
| | 0.1263 | 2.0 | 3126 | 0.1988 | 0.9516 | | |
| ### Framework versions | |
| - Transformers 4.40.0 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.19.0 | |
| - Tokenizers 0.19.1 | |