Instructions to use Denyol/FakeNews-deberta-base-punct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Denyol/FakeNews-deberta-base-punct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Denyol/FakeNews-deberta-base-punct")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Denyol/FakeNews-deberta-base-punct") model = AutoModelForSequenceClassification.from_pretrained("Denyol/FakeNews-deberta-base-punct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Denyol/FakeNews-deberta-base-punct: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/Denyol/FakeNews-deberta-base-punct/resolve/e5cdb6db49bc5dfa08c6363aba2fb5772c8fce7e/README.md
- Command line
-
hf download hf://Denyol/FakeNews-deberta-base-punct@e5cdb6db49bc5dfa08c6363aba2fb5772c8fce7e/README.md
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curl -L -o README.md https://huggingface.co/Denyol/FakeNews-deberta-base-punct/resolve/e5cdb6db49bc5dfa08c6363aba2fb5772c8fce7e/README.md
1.63 kB
metadata
license: mit
base_model: microsoft/deberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: FakeNews-deberta-base-punct
results: []
FakeNews-deberta-base-punct
This model is a fine-tuned version of microsoft/deberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4681
- Accuracy: 0.8374
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.54 | 1.0 | 1605 | 0.4681 | 0.8374 |
| 0.7002 | 2.0 | 3210 | 0.7113 | 0.5234 |
| 0.6967 | 3.0 | 4815 | 0.6938 | 0.5234 |
| 0.6961 | 4.0 | 6420 | 0.6959 | 0.4766 |
| 0.6935 | 5.0 | 8025 | 0.6921 | 0.5234 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1