Text Classification
Transformers
PyTorch
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use EdoAbati/distilbert-base-uncased-finetuned-news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EdoAbati/distilbert-base-uncased-finetuned-news with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EdoAbati/distilbert-base-uncased-finetuned-news")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EdoAbati/distilbert-base-uncased-finetuned-news") model = AutoModelForSequenceClassification.from_pretrained("EdoAbati/distilbert-base-uncased-finetuned-news", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased-finetuned-news
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Framework versions
- Transformers 4.22.0
- Pytorch 1.12.0
- Datasets 2.5.0
- Tokenizers 0.12.1
- Downloads last month
- 6