Text Classification
Transformers
Safetensors
English
distilbert
fine-tuned
text-embeddings-inference
Instructions to use drzeeIslam/distilbert-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use drzeeIslam/distilbert-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="drzeeIslam/distilbert-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("drzeeIslam/distilbert-finetuned") model = AutoModelForSequenceClassification.from_pretrained("drzeeIslam/distilbert-finetuned") - Notebooks
- Google Colab
- Kaggle
DistilBERT Fine-tuned for Text Classification
This is a fine-tuned version of distilbert-base-uncased for a custom text classification task. It has been trained using the Hugging Face Transformers library and logged with Weights & Biases.
Model Details
- Base model: DistilBERT
- Task: Sequence Classification (binary or multi-class)
- Training Framework: Transformers (🤗)
- Logged with: Weights & Biases
Training Info
- Epochs: 1
- Final training loss: ~0.08
- Evaluation: 100% MAP on training data
How to Use
from transformers import pipeline
classifier = pipeline("text-classification", model="drzeeIslam/distilbert-finetuned")
result = classifier("Your input text here")
print(result)
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