Instructions to use duynhatran/deberta_textclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duynhatran/deberta_textclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="duynhatran/deberta_textclassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("duynhatran/deberta_textclassification") model = AutoModelForSequenceClassification.from_pretrained("duynhatran/deberta_textclassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from duynhatran/deberta_textclassification: direct link, hf CLI and curl.
- Browser
- Download file 1.59 kB
-
https://huggingface.co/duynhatran/deberta_textclassification/resolve/main/README.md
- Command line
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hf download hf://duynhatran/deberta_textclassification/README.md
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curl -L -o README.md https://huggingface.co/duynhatran/deberta_textclassification/resolve/main/README.md
1.59 kB
metadata
library_name: transformers
license: mit
base_model: microsoft/deberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: deberta_textclassification
results: []
deberta_textclassification
This model is a fine-tuned version of microsoft/deberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2786
- Accuracy: 0.9467
- F1: 0.9622
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.2349 | 1.0 | 1520 | 0.2316 | 0.9388 | 0.9565 |
| 0.1801 | 2.0 | 3040 | 0.2382 | 0.9467 | 0.9621 |
| 0.0914 | 3.0 | 4560 | 0.2786 | 0.9467 | 0.9622 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.4.0
- Tokenizers 0.19.1