Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use tminhtri1910/codebert_paravul with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tminhtri1910/codebert_paravul with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tminhtri1910/codebert_paravul")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tminhtri1910/codebert_paravul") model = AutoModelForSequenceClassification.from_pretrained("tminhtri1910/codebert_paravul", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from tminhtri1910/codebert_paravul: direct link, hf CLI and curl.
- Browser
- Download file 1.17 kB
-
https://huggingface.co/tminhtri1910/codebert_paravul/resolve/main/README.md
- Command line
-
hf download hf://tminhtri1910/codebert_paravul/README.md
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curl -L -o README.md https://huggingface.co/tminhtri1910/codebert_paravul/resolve/main/README.md
1.17 kB
metadata
library_name: transformers
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
model-index:
- name: codebert_paravul
results: []
codebert_paravul
This model is a fine-tuned version of microsoft/codebert-base on an unknown dataset.
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: 8
- eval_batch_size: 8
- 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: 5
Training results
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2