Instructions to use Patcas/distilbert-3ssclass-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patcas/distilbert-3ssclass-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Patcas/distilbert-3ssclass-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Patcas/distilbert-3ssclass-classification") model = AutoModelForSequenceClassification.from_pretrained("Patcas/distilbert-3ssclass-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Patcas/distilbert-3ssclass-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.53 kB
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https://huggingface.co/Patcas/distilbert-3ssclass-classification/resolve/main/README.md
- Command line
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hf download hf://Patcas/distilbert-3ssclass-classification/README.md
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curl -L -o README.md https://huggingface.co/Patcas/distilbert-3ssclass-classification/resolve/main/README.md
1.53 kB
metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-3ssclass-classification
results: []
distilbert-3ssclass-classification
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1320
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: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 1 | 1.1050 |
| No log | 2.0 | 2 | 1.1226 |
| No log | 3.0 | 3 | 1.1320 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0