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
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https://huggingface.co/ndbao2002/bert-base-vi/resolve/main/README.md
- Command line
-
hf download hf://ndbao2002/bert-base-vi/README.md
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curl -L -o README.md https://huggingface.co/ndbao2002/bert-base-vi/resolve/main/README.md
1.09 kB
| 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 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # bert-base-vi | |
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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 |