Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("pat119/sentiment-classifier-distilbert")
model = AutoModelForSequenceClassification.from_pretrained("pat119/sentiment-classifier-distilbert")Quick Links
sentiment-classifier-distilbert
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.2.2
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
- 3
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pat119/sentiment-classifier-distilbert")