Text Classification
Transformers
PyTorch
TensorBoard
Vietnamese
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ndbao2002/bert-base-vi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ndbao2002/bert-base-vi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ndbao2002/bert-base-vi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ndbao2002/bert-base-vi") model = AutoModelForSequenceClassification.from_pretrained("ndbao2002/bert-base-vi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ndbao2002/bert-base-vi: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
-
https://huggingface.co/ndbao2002/bert-base-vi/resolve/main/README.md
- Command line
-
hf download hf://ndbao2002/bert-base-vi/README.md
-
curl -L -o README.md https://huggingface.co/ndbao2002/bert-base-vi/resolve/main/README.md
1.09 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- vietnamese_students_feedback
model-index:
- name: bert-base-vi
results: []
language:
- vi
pipeline_tag: text-classification
bert-base-vi
This model is a fine-tuned version of bert-base-uncased on the vietnamese_students_feedback dataset.
Intended uses & limitations
This model is used for classifying the semantic of student's feedback on teachers, lectures,...
It's used on Vietnamese language only.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-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: 5
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2