Text Classification
Transformers
TensorBoard
Safetensors
Hausa
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Oloruntobi/bert-hausa-sentimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Oloruntobi/bert-hausa-sentimental with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Oloruntobi/bert-hausa-sentimental")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Oloruntobi/bert-hausa-sentimental") model = AutoModelForSequenceClassification.from_pretrained("Oloruntobi/bert-hausa-sentimental") - Notebooks
- Google Colab
- Kaggle
bert-hausa-sentimental
This model is a fine-tuned version of Davlan/bert-base-multilingual-cased-finetuned-hausa for sentimental analysis in the Hausa langauge.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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: 3.0
Training results
- Accuracy: 0.8372093023255814
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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