Text Classification
Transformers
TensorFlow
distilbert
model
sentiment analysis
bahasa malaysia
text-embeddings-inference
Instructions to use muzanxdem/fine_tuned_ecl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muzanxdem/fine_tuned_ecl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="muzanxdem/fine_tuned_ecl")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("muzanxdem/fine_tuned_ecl") model = AutoModelForSequenceClassification.from_pretrained("muzanxdem/fine_tuned_ecl") - Notebooks
- Google Colab
- Kaggle
Based Model
Model = distilbert-base-uncased
Fine tuned the model from our own custom dataset
with training_args.strategy.scope():
model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
trainer = TFTrainer(
model=model, # the instantiated 🤗 Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=test_dataset # evaluation dataset
)
trainer.train()
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