Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use quikli/text-classification-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quikli/text-classification-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="quikli/text-classification-test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("quikli/text-classification-test") model = AutoModelForSequenceClassification.from_pretrained("quikli/text-classification-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from quikli/text-classification-test: direct link, hf CLI and curl.
- Browser
- Download file 1.6 kB
-
https://huggingface.co/quikli/text-classification-test/resolve/main/README.md
- Command line
-
hf download hf://quikli/text-classification-test/README.md
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curl -L -o README.md https://huggingface.co/quikli/text-classification-test/resolve/main/README.md
1.6 kB
metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: text-classification-test
results: []
text-classification-test
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2371
- Accuracy: 0.9312
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2215 | 1.0 | 1563 | 0.2204 | 0.9137 |
| 0.1468 | 2.0 | 3126 | 0.2371 | 0.9312 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1